diff --git "a/19FLT4oBgHgl3EQfqS-c/content/tmp_files/load_file.txt" "b/19FLT4oBgHgl3EQfqS-c/content/tmp_files/load_file.txt" new file mode 100644--- /dev/null +++ "b/19FLT4oBgHgl3EQfqS-c/content/tmp_files/load_file.txt" @@ -0,0 +1,1010 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf,len=1009 +page_content='Bipol: Multi-axes Evaluation of Bias with Explainability in Benchmark Datasets Tosin Adewumi∗‡, Isabella S¨odergren†, Lama Alkhaled‡, Sana Sabah Sabry‡, Foteini Liwicki‡ and Marcus Liwicki‡ Machine Learning Group, EISLAB, Lule˚a University of Technology, Sweden ∗corresponding author, †isasde-5@student.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='ltu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='se, ‡firstname.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='lastname@ltu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='se Abstract—We evaluate five English NLP benchmark datasets (available on the superGLUE leaderboard) for bias, along mul- tiple axes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The datasets are the following: Boolean Question (Boolq), CommitmentBank (CB), Winograd Schema Challenge (WSC), Winogender diagnostic (AXg), and Recognising Textual Entailment (RTE).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bias can be harmful and it is known to be common in data, which ML models learn from.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' In order to mitigate bias in data, it is crucial to be able to estimate it objectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We use bipol, a novel multi-axes bias metric with explainability, to quantify and explain how much bias exists in these datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Multilingual, multi-axes bias evaluation is not very common.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Hence, we also contribute a new, large labelled Swedish bias-detection dataset, with about 2 million samples;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' translated from the English version.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' In addition, we contribute new multi- axes lexica for bias detection in Swedish.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We train a SotA model on the new dataset for bias detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We make the codes, model, and new dataset publicly available.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Index Terms—bias, explainability, bipol, dataset, nlp I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' INTRODUCTION Bias, which can be harmful [1], is the unfair prejudice in favor of or against a thing, person or group, relative to another [2].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Measuring bias in text data can be challenging because of the axes that may be involved (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' religious or gender bias).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bipol was introduced by [3].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' It is a metric that estimates bias along multiple axes in text data and provides an explanation for its scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' In this work, we investigate and estimate social bias in some of the benchmark datasets for NLP, particularly those available on the English SuperGLUE leaderboard.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The SuperGLUE was introduced by [4] and provides benchmark datasets for different NLP tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Benchmark datasets are datasets for comparing the performance of algorithms for specific use- cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [5], [6].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Such datasets have been the foundation for some of the significant advancements in the field [6].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We investigate the following datasets: Boolq, CB, WSC, AXg, and RTE.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Classification accuracy is known to drop with attempts at mitigating biases in data [7]–[9] yet it is important to estimate and mitigate them because of the ethical implications or harm that may arise for the disadvantaged, sensitive group [10], [11], thereby affecting the data quality.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Some characteristics of bias in text data are:1 It is heavily lopsided.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 1https://libguides.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='uwgb.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='edu/bias It uses inappropriate language.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' It is based on unsubstantiated claims.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' a) Our contributions: We show quantitatively and through explainability that bias exists in the datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' This will provide researchers with insight into how to mitigate bias in text data and possibly add impetus to the conversation on whether it is even ethical to remove these social biases from the training data, because they represent the real world.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Furthermore, we provide, possibly, the largest labelled dataset and lexica for bias detection in Swedish (multi-axes bias dataset (MAB)-Swedish) and train a model based on the state- of-the-art (SotA) Swedish BERT [12].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We release our codes publicly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='2 The rest of this paper is structured as follows: Section II describes materials used and our methods, including details of the characteristics of bipol and the new MAB-Swedish dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Section III describes the results and discusses the types of bias in the datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Section IV discusses some of the previous related work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' In Section V, we conclude our work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' II.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' MATERIALS & METHODS A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bipol There are two stages in the implementation of bipol (see 1a [3]) before it gives a final score between 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0 (zero or un- detected bias) and 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0 (extreme bias).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The first stage involves the classification of the data samples (into biased and unbiased categories) using a trained model (see 1b).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' It is the ratio of the number of biased samples (true positives (tp) and false positives (fp)) to the total samples (true positives (tp), false positives (fp), true negatives (tn), and false negatives (fn)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Ideally, a good classifier should minimize the number of fp and maximize the number of tp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The second stage evaluates the biased samples for sensitive terms listed in the multi-axes lexica (see 1c).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' It involves finding the difference between the two maximum summed frequencies in the types of an axis (| �n s=1 as − �m s=1 bs|), which is then divided by the summed frequencies of all the terms in that axis (�p s=1 ds).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The average over all the axes ( 1 q �q x=1) using this operation is then averaged over all the biased samples ( 1 r �r t=1 ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Table I provides the Swedish lexica sizes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The lexica are 2github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='com/tosingithub/Bipol arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='12139v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='CL] 28 Jan 2023 derived from [13], [14] and Wikipedia3 and may be expanded as needed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The English lexica contain more and are derived from public sources [3].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' b = bc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='bs (1a) bc = tp + fp tp + fp + tn + fn (1b) bs = 1 r r � t=1 � 1 q q � x=1 �| �n s=1 as − �m s=1 bs| �p s=1 ds � x � t (1c) TABLE I SWEDISH LEXICA SIZES.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' THESE MAY BE EXPANDED.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Axis Axis type 1 Axis type 2 Gender 17 (female) 19 (male) Racial 10 (black) 10 (white) The rationale for using bipol is because of the strengths of the metric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' These include 1) the relative simplicity of calculating a score, 2) it is straight-forward to implement since it is based on existing concepts like lexica and classifiers, 3) it captures semantic and term frequency (TF) aspects of data, and 4) it is not limited in the total number of axes that may be used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We acknowledge, however, that it has limitations that are based on the limitations of the tools that may be used to calculate it.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Datasets The new MAB-Swedish dataset: The dataset was machine- translated (from MAB [3]) using the Helsinki-NLP model [15], which was mostly trained with guided alignment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The automatic translation took over 48 hours on one GPU.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The features in the two datasets are, hence, the same.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' It has 1,946,975 samples, as given in Table II.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The English version was constructed from two datasets: Jigsaw4 and the Social Bias Inference Corpus v2 (SBICv2) by [16].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The Jigsaw (a multipurpose dataset) and the SBICv2 assume that bias correlates with toxicity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The samples with toxicity equal to or above the threshold of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='1 were automatically labelled as biased and those below were labelled as unbiased.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Some examples in the MAB-Swedish are given in Table III.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' TABLE II MAB-SWEDISH & MAB DATASETS SPLIT Set Biased Unbiased Total Training 533,544 1,209,433 1,742,977 Validation 32338 69649 101987 Test 33470 68541 102011 599,352 1,347,623 1,946,975 3en.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='wikipedia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='org/wiki/Swedish profanity 4medium.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='com/jigsaw/creating-labeled-datasets-and-exploring-the-role-of- human-raters-56367b6db298 TABLE III EXAMPLES FROM THE MAB-SWEDISH (THE ENGLISH IN THE ORIGINAL IS IN ITALICS) comment text label old id id Den h¨ar slynan ¨ar galen som skulle l¨asa en bok av en kvinna.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' biased 239612 1212584 (This b*tch is nuts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Who would read a book by a woman.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=') 9 av 10 m¨anniskor njuter av mobbning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Jag kommer alltid att vara den 10: e biased none 1517517 (9 out of 10 people enjoy bully- ing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' I will always be the 10th.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=') ”F¨orfattaren s¨ager att det finns ¿ m˚anga, m˚anga fler anled- ningar till att jag antar att en uppf¨oljare finns i verken, d˚a?”' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' unbiased 383811 110831 (”The author says that there are many, many more reasons I am assuming a sequel is in the works, then?”' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=') Vad kallar du underkl¨ader f¨or araber?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' biased none 1618146 (What do you call lingerie for Arabs?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Socks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=') C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Experiments The experiments are conducted on two shared Nvidia DGX- 1 clusters running Ubuntu 18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='04 and 20.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='04 with 8 × 32GB V100 and 8 x 40GB A100 GPUs, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Average results are reported after running each experiment twice.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' To evaluate the benchmark datasets, we use RoBERTa, DeBERTa, and Electra bias-detection trained models [3].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Wandb [17], an experiment tracking tool, is run for 5 counts for training Swedish BERT with bayesian optimization to suggest the best hyper-parameter combination for the initial learning rate (1e-3 - 2e-5) and epochs (6 - 10), since it has been observed that hyper-parameters strongly influence per- formance [18].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Figure 13 (in the appendix) shows the wandb exploration for Swedish BERT on MAB-Swedish in parallel coordinates.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We use the pretrained base Swedish BERT [12] from the HuggingFace hub [19].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Average training time was 15 hours.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Average evaluation time ranges from about 30 minutes to over 24 hours for the English benchmark datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='5 III.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' RESULTS AND DISCUSSION The macro F1 score on the validation set of MAB-Swedish is 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='8688 and standard deviation (s.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='d.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=') of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0005 (see 13).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' From Table IV we observe that all the datasets have bias, though little.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The dataset with the least amount of bias is Boolq, which is confirmed by all the three models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' This is despite the dataset having the highest number of unique samples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' CB has the largest amount of bias and this is also confirmed by the three models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 5particularly when cpulimit is used, in fairness to other users TABLE IV RESULTS OF AVERAGE SCORES.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' bipol level ↓ (s.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='d.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=') RoBERTa unique samples corpus sentence bipol (b) Boolq 7,929 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0066 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='8027 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0053 (0) CB 250 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='08 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='8483 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0679 (0) WSC 279 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0466 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='8718 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0406 (0) AXg 178 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0112 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0112 (0) RTE 2,379 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0294 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='8518 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0251 (0) DeBERTa Boolq 7,929 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0103 0.' metadata={'source': 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metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0582 (0) AXg 178 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0112 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0112 (0) RTE 2,379 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0269 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='8593 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='0231 (0) Explaining bias type The type of overall bias (for the gender axis) in many of the datasets is explained by the dictionary of lists produced by bipol (see Appendix B) and represented in ”top-5 frequent terms” bar graphs of Figures 1 to 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We observe from Figures 1, 2, and 3 that Boolq is male-biased.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Figures 4, 5, and 6 show that CB is also male-biased.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' This is the case also for RTE, as revealed by Figures 7, 8, and 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' On the other hand, we observe that the case of WSC is not clear-cut because Figure 10 shows only a marginal lead for female bias, Figure 11 shows the difference among the top-5 is zero and Figure 12 shows a slight overall male bias.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in Boolq by RoBERTa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' RELATED WORK Bias can lead to unfair treatment based on factors such as gender, age, race, etc [3].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Determining the level of bias in NLP datasets along these multiple axes can be a significant challenge but there has been considerable effort in identifying and analyzing bias along some of these axes [20]–[23].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Studies have demonstrated that the biases in language models for the intersection of gender and race can be greater than those for Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in Boolq by DeBERTa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in Boolq by Electra.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in CB by Roberta.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in CB by DeBERTa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in CB by Electra.' metadata={'source': 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'/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='malel female ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='Term ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='Male ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='FemaleFig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in RTE by RoBERTa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in RTE by DeBERTa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in RTE by Electra.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in WSC by RoBERTa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in WSC by DeBERTa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Top-5 gender frequent terms in WSC by Electra.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' gender and race individually and that addressing bias along only one axis can lead to more issues [24], [25].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Our work does not limit the number of axes that can be evaluated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Addressing bias in the English language is not sufficient.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [26] proposed a multi-language approach using HurtLex [27].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' In the English language, there are common biases that asso- ciate female terms with subjects such as liberal arts and family and male terms with subjects such as science [28].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' There are also more words that sexualize females more than males [22].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Other languages have their own peculiarities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' There are various methods for quantifying the extent of discrimination or bias that is present in a dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' One method is odds ratio (OR), which compares the chance of a specific outcome happening, with a certain exposure, to the likelihood of that outcome happening without the exposure [29].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Another method is the impact ratio (IR), which calculates the ratio of positive outcomes for a protected group to the general group.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' In [20], they compare lexicon method to model classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Our approach combines the strengths of both approaches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Other researchers have quantitatively shown the bias present in the geometry of word embeddings, which may amplify different gender or demographic stereotypes [30]–[32].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' To address the bias in word embeddings, [33] suggests debiasing by removing gender from the embeddings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' CONCLUSION We show that all benchmark datasets we evaluated, which are available on the SuperGLUE leaderboard, contain bias to different degrees.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' This is the first time these datasets are evaluated in such a way that quantifies the amount of bias and the type.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We believe these evaluations will motivate research on how to more effectively mitigate bias along multiple axes in datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Our public release of the new MAB-Swedish dataset, lexica and model will also facilitate future work in multilingual bias detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' REFERENCES [1] N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Mehrabi, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Morstatter, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Saxena, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Lerman, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Galstyan, “A survey on bias and fairness in machine learning,” ACM Computing Surveys (CSUR), vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 54, no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 6, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 1–35, 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [2] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Adewumi, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Liwicki, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Liwicki, “Conversational systems in machine learning from the point of view of the philosophy of science—using alime chat and related studies,” Philosophies, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 4, no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 3, p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 41, 2019.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [3] L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Alkhaled, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Adewumi, and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Sabah Sabry, “Bipol: A novel multi- axes bias evaluation metric with explainability for nlp,” Manuscript, 2023.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='5 2 2 Frequency 1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='5 1 １ 0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='5 ' metadata={'source': 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+page_content='neurips.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='cc/paper/2019/ file/4496bf24afe7fab6f046bf4923da8de6-Paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='pdf [5] S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Dhar and L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Shamir, “Evaluation of the benchmark datasets for testing the efficacy of deep convolutional neural networks,” Visual Informatics, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 5, no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 3, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 92–101, 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [6] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Paullada, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Raji, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bender, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Denton, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Hanna, “Data and its (dis) contents: A survey of dataset development and use in machine learning research,” Patterns, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2, no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 11, p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 100336, 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [7] G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Pleiss, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Raghavan, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Wu, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Kleinberg, and K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Weinberger, “On fairness and calibration,” in Advances in Neural Information Processing Systems, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Guyon, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Luxburg, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bengio, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Wallach, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Fergus, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Vishwanathan, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Garnett, Eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=', vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 30.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Curran Associates, Inc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=', 2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Available: https://proceedings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='neurips.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' cc/paper/2017/file/b8b9c74ac526fffbeb2d39ab038d1cd7-Paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='pdf [8] L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Oneto, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Doninini, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Elders, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Pontil, “Taking advantage of multitask learning for fair classification,” in Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 227–237.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [9] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Speicher, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Heidari, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Grgic-Hlaca, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Gummadi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Singla, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Weller, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Zafar, “A unified approach to quantifying algorith- mic unfairness: Measuring individual &group unfairness via inequality indices,” in Proceedings of the 24th ACM SIGKDD international con- ference on knowledge discovery & data mining, 2018, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2239–2248.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [10] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Klare, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Burge, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Klontz, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bruegge, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Jain, “Face recognition performance: Role of demographic information,” IEEE Transactions on Information Forensics and Security, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 7, no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 6, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 1789–1801, 2012.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [11] I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Raji, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Gebru, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Mitchell, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Buolamwini, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Lee, and E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Denton, “Saving face: Investigating the ethical concerns of facial recognition auditing,” in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, ser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' AIES ’20.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' New York, NY, USA: Association for Computing Machinery, 2020, p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 145–151.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Available: https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='1145/3375627.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='3375820 [12] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Malmsten, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' B¨orjeson, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Haffenden, “Playing with words at the national library of sweden–making a swedish bert,” arXiv preprint arXiv:2007.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='01658, 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [13] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Adewumi, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Liwicki, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Liwicki, “Corpora compared: The case of the swedish gigaword & wikipedia corpora,” arXiv preprint arXiv:2011.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='03281, 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [14] ——, “Exploring swedish & english fasttext embeddings for ner with the transformer,” arXiv preprint arXiv:2007.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='16007, 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [15] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Tiedemann and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Thottingal, “OPUS-MT — Building open transla- tion services for the World,” in Proceedings of the 22nd Annual Con- ferenec of the European Association for Machine Translation (EAMT), Lisbon, Portugal, 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [16] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Sap, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Gabriel, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Qin, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Jurafsky, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Smith, and Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Choi, “Social bias frames: Reasoning about social and power implications of language,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Online: Association for Computational Linguistics, Jul.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 5477–5490.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Available: https://www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='aclweb.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='org/anthology/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='acl-main.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='486 [17] L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Biewald, “Experiment tracking with weights and biases,” 2020, software available from wandb.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='com.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Available: https: //www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='wandb.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='com/ [18] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Adewumi, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Liwicki, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Liwicki, “Word2vec: Optimal hyperparameters and their impact on natural language processing downstream tasks,” Open Computer Science, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 12, no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 1, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 134– 141, 2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Available: https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='1515/comp-2022-0236 [19] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Wolf, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Debut, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Sanh, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Chaumond, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Delangue, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Moi, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Cistac, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Rault, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Louf, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Funtowicz, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Davison, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Shleifer, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' von Platen, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Ma, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Jernite, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Plu, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Xu, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Le Scao, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Gugger, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Drame, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Lhoest, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Rush, “Transformers: State- of-the-art natural language processing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Online: Association for Computational Linguistics, Oct.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2020, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 38–45.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Available: https: //aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='org/2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='emnlp-demos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='6 [20] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Cryan, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Tang, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Zhang, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Metzger, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Zheng, and B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Zhao, “Detecting gender stereotypes: Lexicon vs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' supervised learning methods,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, ser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' CHI ’20.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' New York, NY, USA: Association for Computing Machinery, 2020, p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 1–11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Available: https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='1145/3313831.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='3376488 [21] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Dhamala, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Sun, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Kumar, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Krishna, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Pruksachatkun, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='-W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Chang, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Gupta, “Bold: Dataset and metrics for measuring biases in open-ended language generation,” in ACM FAccT 2021, 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Available: https://www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='amazon.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='science/publications/ bold-dataset-and-metrics-for-measuring-biases-in-open-ended-language-generation [22] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Stanley, “Paradigmatic woman: The prostitute,” Papers in language variation, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 303–321, 1977.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [23] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Chandrabose, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Chakravarthi et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=', “An overview of fairness in data–illuminating the bias in data pipeline,” in Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion, 2021, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 34–45.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [24] Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Tan and L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Celis, “Assessing social and intersectional biases in contextualized word representations,” Advances in Neural Information Processing Systems, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 32, 2019.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [25] S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Subramanian, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Han, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Baldwin, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Cohn, and L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Frermann, “Eval- uating debiasing techniques for intersectional biases,” arXiv preprint arXiv:2109.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='10441, 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [26] D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Nozza, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bianchi, and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Hovy, “Honest: Measuring hurtful sentence completion in language models,” in The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Association for Computational Lin- guistics, 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [27] E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bassignana, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Basile, and V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Patti, “Hurtlex: A multilingual lexicon of words to hurt,” in 5th Italian Conference on Computational Linguis- tics, CLiC-it 2018, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2253.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' CEUR-WS, 2018, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 1–6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [28] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Nosek, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Banaji, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Greenwald, “Harvesting implicit group attitudes and beliefs from a demonstration web site.” Group Dynamics: Theory, Research, and Practice, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 6, no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 1, p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 101, 2002.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [29] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Szumilas, “Explaining odds ratios,” Journal of the Canadian academy of child and adolescent psychiatry, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 19, no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 3, p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 227, 2010.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [30] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bolukbasi, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='-W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Chang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Zou, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Saligrama, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Kalai, “Man is to computer programmer as woman is to homemaker?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' debiasing word embeddings,” Advances in neural information processing systems, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 29, 2016.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [31] E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Bender, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Gebru, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' McMillan-Major, and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Shmitchell, “On the dangers of stochastic parrots: Can language models be too big?”' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 610–623.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [32] S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Blodgett, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Barocas, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Daum´e III, and H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Wallach, “Language (technology) is power: A critical survey of” bias” in nlp,” arXiv preprint arXiv:2005.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='14050, 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [33] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Schmidt, “Rejecting the gender binary: a vector-space operation,” Ben’s Bookworm Blog, 2015.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [34] C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Clark, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Lee, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='-W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Chang, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Kwiatkowski, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Collins, and K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Toutanova, “BoolQ: Exploring the surprising difficulty of natural yes/no questions,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Minneapolis, Minnesota: Association for Computational Linguistics, Jun.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2019, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2924–2936.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [Online].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Available: https: //aclanthology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='org/N19-1300 [35] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='-C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' De Marneffe, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Simons, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Tonhauser, “The commit- mentbank: Investigating projection in naturally occurring discourse,” in proceedings of Sinn und Bedeutung, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 23, no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2, 2019, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 107–124.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [36] H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Levesque, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Davis, and L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Morgenstern, “The winograd schema challenge,” in Thirteenth international conference on the principles of knowledge representation and reasoning, 2012.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' [37] R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Rudinger, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Naradowsky, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Leonard, and B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Van Durme, “Gender bias in coreference resolution,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' New Orleans, Louisiana: Association for Computational Linguistics, June 2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' APPENDIX A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Data 1) BoolQ (Boolean Questions): is a question-answering (QA) task where each example has a short passage and a yes/no question about the passage [34] .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' These questions were provided anonymously by Google search users and afterwards paired with a paragraph from a Wikipedia article that has the answer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We evaluated the passage column of the dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 2) CB: [35]: contains short texts in which at least one sentence has an embedded clause.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' The resulting task is framed as three-class textual entailment on examples that are drawn from the following datasets: Wall Street Journal, fiction from the British National Corpus, and Switchboard.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We evaluated the premise column of the dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 3) WSC: [36]: is a coreference resolution dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Exam- ples consist of a sentence with a pronoun and a list of noun phrases from the sentence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We evaluated the text column of the dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 4) AXg: [37]: It is designed to measure gender bias in coreference resolution systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Each example consists of a premise sentence having a male or female pronoun and a hypothesis giving a possible antecedent of the pronoun.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We evaluated the premise column of the dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' 5) RTE [4]: : datasets come from a series of annual compe- titions on textual entailment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Data from several sources were merged and converted to two-class classification: entailment and not entailment to obtain this dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' We evaluated the premise column of the dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' Experiment Dictionary of lists for RoBERTa on Boolq: {’gender’: [’ she ’: 23,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ’ her ’: 17,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ’ woman ’: 2,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ’ lady ’: 1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ’ female ’: 6,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ’ girl ’: 1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ’ skirt ’: 0,' metadata={'source': 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