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+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.ltu.se, ‡firstname.lastname@ltu.se +Abstract—We evaluate five English NLP benchmark datasets +(available on the superGLUE leaderboard) for bias, along mul- +tiple axes. The datasets are the following: Boolean Question +(Boolq), CommitmentBank (CB), Winograd Schema Challenge +(WSC), Winogender diagnostic (AXg), and Recognising Textual +Entailment (RTE). Bias can be harmful and it is known to be +common in data, which ML models learn from. In order to +mitigate bias in data, it is crucial to be able to estimate it +objectively. We use bipol, a novel multi-axes bias metric with +explainability, to quantify and explain how much bias exists in +these datasets. Multilingual, multi-axes bias evaluation is not very +common. Hence, we also contribute a new, large labelled Swedish +bias-detection dataset, with about 2 million samples; translated +from the English version. In addition, we contribute new multi- +axes lexica for bias detection in Swedish. We train a SotA model +on the new dataset for bias detection. We make the codes, model, +and new dataset publicly available. +Index Terms—bias, explainability, bipol, dataset, nlp +I. 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]. Measuring bias in text data can be challenging because of +the axes that may be involved (e.g. religious or gender bias). +Bipol was introduced by [3]. It is a metric that estimates bias +along multiple axes in text data and provides an explanation +for its scores. +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. The SuperGLUE +was introduced by [4] and provides benchmark datasets for +different NLP tasks. Benchmark datasets are datasets for +comparing the performance of algorithms for specific use- +cases. [5], [6]. Such datasets have been the foundation for +some of the significant advancements in the field [6]. We +investigate the following datasets: Boolq, CB, WSC, AXg, and +RTE. +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. Some characteristics of bias +in text data are:1 +• It is heavily lopsided. +1https://libguides.uwgb.edu/bias +• It uses inappropriate language. +• It is based on unsubstantiated claims. +a) Our +contributions: +We +show +quantitatively +and +through explainability that bias exists in the datasets. 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. +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]. We release our codes +publicly.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. +Section III describes the results and discusses the types of bias +in the datasets. Section IV discusses some of the previous +related work. In Section V, we conclude our work. +II. MATERIALS & METHODS +A. Bipol +There are two stages in the implementation of bipol (see +1a [3]) before it gives a final score between 0.0 (zero or un- +detected bias) and 1.0 (extreme bias). The first stage involves +the classification of the data samples (into biased and unbiased +categories) using a trained model (see 1b). 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)). +Ideally, a good classifier should minimize the number of fp +and maximize the number of tp. +The second stage evaluates the biased samples for sensitive +terms listed in the multi-axes lexica (see 1c). 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). 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 +). Table I provides the Swedish lexica sizes. The lexica are +2github.com/tosingithub/Bipol +arXiv:2301.12139v1 [cs.CL] 28 Jan 2023 + +derived from [13], [14] and Wikipedia3 and may be expanded +as needed. The English lexica contain more and are derived +from public sources [3]. +b = bc.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. THESE MAY BE EXPANDED. +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. 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. We acknowledge, however, that it has limitations that +are based on the limitations of the tools that may be used to +calculate it. +B. 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. The +automatic translation took over 48 hours on one GPU. The +features in the two datasets are, hence, the same. It has +1,946,975 samples, as given in Table II. The English version +was constructed from two datasets: Jigsaw4 and the Social +Bias Inference Corpus v2 (SBICv2) by [16]. The Jigsaw +(a multipurpose dataset) and the SBICv2 assume that bias +correlates with toxicity. The samples with toxicity equal to +or above the threshold of 0.1 were automatically labelled +as biased and those below were labelled as unbiased. Some +examples in the MAB-Swedish are given in Table III. +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.wikipedia.org/wiki/Swedish profanity +4medium.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. +biased +239612 +1212584 +(This b*tch is nuts. Who would +read a book by a woman.) +9 av 10 m¨anniskor njuter av +mobbning. Jag kommer alltid +att vara den 10: e +biased +none +1517517 +(9 out of 10 people enjoy bully- +ing. I will always be the 10th.) +”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?” +unbiased +383811 +110831 +(”The author says that there +are many, many more reasons I +am assuming a sequel is in the +works, then?”) +Vad kallar du underkl¨ader f¨or +araber? +biased +none +1618146 +(What do you call lingerie for +Arabs? Socks.) +C. Experiments +The experiments are conducted on two shared Nvidia DGX- +1 clusters running Ubuntu 18.04 and 20.04 with 8 × 32GB +V100 and 8 x 40GB A100 GPUs, respectively. Average results +are reported after running each experiment twice. To evaluate +the benchmark datasets, we use RoBERTa, DeBERTa, and +Electra bias-detection trained models [3]. +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]. Figure 13 (in the appendix) shows the wandb +exploration for Swedish BERT on MAB-Swedish in parallel +coordinates. We use the pretrained base Swedish BERT [12] +from the HuggingFace hub [19]. Average training time was 15 +hours. Average evaluation time ranges from about 30 minutes +to over 24 hours for the English benchmark datasets.5 +III. RESULTS AND DISCUSSION +The macro F1 score on the validation set of MAB-Swedish +is 0.8688 and standard deviation (s.d.) of 0.0005 (see 13). +From Table IV we observe that all the datasets have bias, +though little. The dataset with the least amount of bias is +Boolq, which is confirmed by all the three models. This +is despite the dataset having the highest number of unique +samples. CB has the largest amount of bias and this is also +confirmed by the three models. +5particularly when cpulimit is used, in fairness to other users + +TABLE IV +RESULTS OF AVERAGE SCORES. +bipol level ↓ (s.d.) +RoBERTa +unique samples +corpus +sentence +bipol (b) +Boolq +7,929 +0.0066 +0.8027 +0.0053 (0) +CB +250 +0.08 +0.8483 +0.0679 (0) +WSC +279 +0.0466 +0.8718 +0.0406 (0) +AXg +178 +0.0112 +1 +0.0112 (0) +RTE +2,379 +0.0294 +0.8518 +0.0251 (0) +DeBERTa +Boolq +7,929 +0.0103 +0.7212 +0.0075 (0) +CB +250 +0.084 +0.9048 +0.076 (0) +WSC +279 +0.0609 +1 +0.0609 (0) +AXg +178 +0.0112 +1 +0.0112 (0) +RTE +2,379 +0.0366 +0.8655 +0.0316 (0) +Electra +Boolq +7,929 +0.0073 +0.8089 +0.0059 (0) +CB +250 +0.0316 +0.881 +0.074 (0) +WSC +279 +0.0609 +0.9559 +0.0582 (0) +AXg +178 +0.0112 +1 +0.0112 (0) +RTE +2,379 +0.0269 +0.8593 +0.0231 (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. We observe from Figures +1, 2, and 3 that Boolq is male-biased. Figures 4, 5, and 6 show +that CB is also male-biased. This is the case also for RTE, as +revealed by Figures 7, 8, and 9. 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. +Fig. 1. Top-5 gender frequent terms in Boolq by RoBERTa. +IV. RELATED WORK +Bias can lead to unfair treatment based on factors such as +gender, age, race, etc [3]. 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]. Studies +have demonstrated that the biases in language models for the +intersection of gender and race can be greater than those for +Fig. 2. Top-5 gender frequent terms in Boolq by DeBERTa. +Fig. 3. Top-5 gender frequent terms in Boolq by Electra. +Fig. 4. Top-5 gender frequent terms in CB by Roberta. +Fig. 5. Top-5 gender frequent terms in CB by DeBERTa. +Fig. 6. Top-5 gender frequent terms in CB by Electra. + +90 +80 +80 +70 +60 +49 +50 +40 +30 +23 +17 +20 +10 +6 +8 +5 +10 +3 +3 +0 +hel she +him I her +male I female +boy I wife +jackl love +Term +Male +Female100 +93 +89 +81 +77 +80 +Frequency +60 +40 +20 +13 +11 +10 +9 +3 +4 +0 +hel she +him I her +malel love +jackI female +guyl woman +Term +Male +Female80 +70 +70 +60 +60 +51 +Frequency +50 +43 +40 +30 +20 +10 +7 +8 +6 +10 +3 +2 +0 +hel she +him I her +malel love +jackl female +guyl woman +Male +Female20 +17 +15 +Frequency +10 +8 +7 +5 +1 +0 +0 +0 +0 +0 +0 +hel she +him I her +boy I girl +manI woman +male I female +Term +Male +Female20 +16 +15 +10 +Frequency +5 +4 +5 +1 +1 +0 +0 +0 +0 +0 +0 +hel she +him I her +boyI girl +manI woman +maleI female +Term +IMale +Female16 +14 +14 +12 +Frequency +10 +8 +6 +6 +4 +4 +1 +2 +1 +0 +0 +0 +0 +0 +0 +hel she +him I her +boyl girl +manI woman +malel female +Term +Male +FemaleFig. 7. Top-5 gender frequent terms in RTE by RoBERTa. +Fig. 8. Top-5 gender frequent terms in RTE by DeBERTa. +Fig. 9. Top-5 gender frequent terms in RTE by Electra. +Fig. 10. Top-5 gender frequent terms in WSC by RoBERTa. +Fig. 11. Top-5 gender frequent terms in WSC by DeBERTa. +Fig. 12. Top-5 gender frequent terms in WSC by Electra. +gender and race individually and that addressing bias along +only one axis can lead to more issues [24], [25]. Our work +does not limit the number of axes that can be evaluated. +Addressing bias in the English language is not sufficient. +[26] proposed a multi-language approach using HurtLex [27]. +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]. There are +also more words that sexualize females more than males [22]. +Other languages have their own peculiarities. +There are various methods for quantifying the extent of +discrimination or bias that is present in a dataset. 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]. Another +method is the impact ratio (IR), which calculates the ratio of +positive outcomes for a protected group to the general group. +In [20], they compare lexicon method to model classification. +Our approach combines the strengths of both approaches. +Other researchers have quantitatively shown the bias present +in the geometry of word embeddings, which may amplify +different gender or demographic stereotypes [30]–[32]. To +address the bias in word embeddings, [33] suggests debiasing +by removing gender from the embeddings. +V. CONCLUSION +We show that all benchmark datasets we evaluated, which +are available on the SuperGLUE leaderboard, contain bias +to different degrees. This is the first time these datasets are +evaluated in such a way that quantifies the amount of bias and +the type. We believe these evaluations will motivate research +on how to more effectively mitigate bias along multiple axes in +datasets. Our public release of the new MAB-Swedish dataset, +lexica and model will also facilitate future work in multilingual +bias detection. +REFERENCES +[1] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, +“A survey on bias and fairness in machine learning,” ACM Computing +Surveys (CSUR), vol. 54, no. 6, pp. 1–35, 2021. +[2] T. P. Adewumi, F. Liwicki, and M. Liwicki, “Conversational systems +in machine learning from the point of view of the philosophy of +science—using alime chat and related studies,” Philosophies, vol. 4, +no. 3, p. 41, 2019. +[3] L. Alkhaled, T. Adewumi, and S. 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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). +Minneapolis, Minnesota: Association for Computational +Linguistics, Jun. 2019, pp. 2924–2936. [Online]. Available: https: +//aclanthology.org/N19-1300 +[35] M.-C. De Marneffe, M. Simons, and J. Tonhauser, “The commit- +mentbank: Investigating projection in naturally occurring discourse,” in +proceedings of Sinn und Bedeutung, vol. 23, no. 2, 2019, pp. 107–124. +[36] H. Levesque, E. Davis, and L. Morgenstern, “The winograd schema +challenge,” in Thirteenth international conference on the principles of +knowledge representation and reasoning, 2012. +[37] R. Rudinger, J. Naradowsky, B. Leonard, and B. 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. +New Orleans, Louisiana: +Association for Computational Linguistics, June 2018. +APPENDIX +A. 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] . These questions were +provided anonymously by Google search users and afterwards +paired with a paragraph from a Wikipedia article that has the +answer. We evaluated the passage column of the dataset. +2) CB: +[35]: contains short texts in which at least one +sentence has an embedded clause. 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. We evaluated +the premise column of the dataset. +3) WSC: +[36]: is a coreference resolution dataset. Exam- +ples consist of a sentence with a pronoun and a list of noun +phrases from the sentence. We evaluated the text column of +the dataset. +4) AXg: +[37]: It is designed to measure gender bias in +coreference resolution systems. Each example consists of a +premise sentence having a male or female pronoun and a +hypothesis giving a possible antecedent of the pronoun. We +evaluated the premise column of the dataset. +5) RTE [4]: : datasets come from a series of annual compe- +titions on textual entailment. Data from several sources were +merged and converted to two-class classification: entailment +and not entailment to obtain this dataset. We evaluated the +premise column of the dataset. +B. Experiment +Dictionary of lists for RoBERTa on Boolq: {’gender’: [’ +she ’: 23, ’ her ’: 17, ’ woman ’: 2, ’ lady ’: 1, ’ female ’: +6, ’ girl ’: 1, ’ skirt ’: 0, ’ madam ’: 0, ’ gentlewoman ’: 0, +’ madame ’: 0, ’ dame ’: 0, ’ gal ’: 0, ’ maiden ’: 0, ’ maid +’: 0, ’ damsel ’: 0, ’ senora ’: 0, ’ lass ’: 0, ’ beauty ’: 0, ’ +ingenue ’: 0, ’ belle ’: 0, ’ doll ’: 0, ’ se˜nora ’: 0, ’ senorita +’: 0, ’ lassie ’: 0, ’ ing´enue ’: 0, ’ miss ’: 0, ’ mademoiselle +’: 0, ’ se˜norita ’: 0, ’ babe ’: 0, ’ girlfriend ’: 0, ’ lover ’: 0, ’ +mistress ’: 0, ’ ladylove ’: 0, ’ inamorata ’: 0, ’ gill ’: 0, ’ old +’: 2, ’ beloved ’: 0, ’ dear ’: 0, ’ sweetheart ’: 0, ’ sweet ’: 0, +’ flame ’: 2, ’ love ’: 5, ’ valentine ’: 0, ’ favorite ’: 1, ’ moll +’: 0, ’ darling ’: 0, ’ honey ’: 0, ’ significant ’: 0, ’ wife ’: 3, +’ wifey ’: 0, ’ missus ’: 0, ’ helpmate ’: 0, ’ helpmeet ’: 0, ’ +spouse ’: 0, ’ bride ’: 1, ’ partner ’: 0, ’ missis ’: 0, ’ widow +’: 0, ’ housewife ’: 0, ’ mrs ’: 0, ’ matron ’: 0, ’ soul ’: 3, ’ +mate ’: 1, ’ housekeeper ’: 0, ’ dowager ’: 0, ’ companion ’: 0, +’ homemaker ’: 0, ’ consort ’: 0, ’ better half ’: 0, ’ hausfrau +’: 0, ’ stay-at-home ’: 0, ’ he ’: 80, ’ him ’: 49, ’ boy ’: 3, +’ man ’: 1, ’ male ’: 10, ’ guy ’: 1, ’ masculine ’: 0, ’ virile +’: 0, ’ manly ’: 0, ’ man-sized ’: 0, ’ hypermasculine ’: 0, ’ +macho ’: 0, ’ mannish ’: 0, ’ manlike ’: 0, ’ man-size ’: 0, ’ +hairy-chested ’: 0, ’ butch ’: 0, ’ ultramasculine ’: 0, ’ boyish +’: 0, ’ tomboyish ’: 0, ’ hoydenish ’: 0, ’ amazonian ’: 0, ’ +gentleman ’: 0, ’ dude ’: 0, ’ fellow ’: 0, ’ cat ’: 2, ’ gent ’: +0, ’ fella ’: 0, ’ lad ’: 0, ’ bloke ’: 0, ’ bastard ’: 0, ’ joe ’: 0, +’ chap ’: 0, ’ chappie ’: 0, ’ hombre ’: 0, ’ galoot ’: 0, ’ buck +’: 0, ’ joker ’: 3, ’ mister ’: 0, ’ jack ’: 8, ’ sir ’: 0, ’ master +’: 1, ’ buddy ’: 0, ’ buster ’: 0], ’racial’:... } + +Fig. 13. WandB parallel coordinates for Swedish BERT training on MAB-Swedish. + +learning_rate +f1 +num_train_epochs +0.00090 +0°6 +0.86930 +8.8 +0.86920 +0.00080 +8.6 +0.86910 +8.4 - +0.00070 +0.86900 +8.2 +0.86890 +0.00060 +8.0- +0.86880 +7.8 +0.00050 +Q.86870 +7.6 +7.4 +0.86860 +0.00040 +7.2 +0.86850 +0.00030 +7.0 +0.86840 +6.8 +0.86830 +0.00020 +6.6 +0.86820 +6.4 +0.00010 +0.86810 +6.2 +0.00000 +6.0 +0.86800 \ No newline at end of file diff --git a/19FLT4oBgHgl3EQfqS-c/content/tmp_files/load_file.txt b/19FLT4oBgHgl3EQfqS-c/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..cee52b0740f93381675afc2b238ff368606370f5 --- /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': 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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': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='90 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='80 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content='80 ' 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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, 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'/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': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ’ madam ’: 0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ’ gentlewoman ’: 0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} +page_content=' ’ madame ’: 0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/19FLT4oBgHgl3EQfqS-c/content/2301.12139v1.pdf'} 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+oid sha256:045c7fe86272ff156bfa76cd35bb5a0347bb34b8ef96231ece83d0e72d37c617 +size 7471149 diff --git a/3NAzT4oBgHgl3EQfuf1J/content/tmp_files/2301.01691v1.pdf.txt b/3NAzT4oBgHgl3EQfuf1J/content/tmp_files/2301.01691v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..c306aa9733b9053921071efdd4e34af9f5660bcb --- /dev/null +++ b/3NAzT4oBgHgl3EQfuf1J/content/tmp_files/2301.01691v1.pdf.txt @@ -0,0 +1,1040 @@ +arXiv:2301.01691v1 [physics.plasm-ph] 4 Jan 2023 +Special behavior of alkali beam emission spectroscopy in low-ion-temperature plasma +P. Bal´azs1,2, O. Asztalos1,2, G. Anda2, M. Vecsei2, S. Zoletnik2, S. T. A. Kumar3, G. I. Pokol1,2 +1Institute of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary +2Centre for Energy Research, Budapest, Hungary and +3Department of Electrical and Computer Engineering, HSX Plasma Laboratory, +University of Wisconsin-Madison, Madison, WI, United States of America +Beam emission spectroscopy (BES) is a powerful plasma diagnostic method especially suited for +the measurement of plasma density and its fluctuations. Designing a BES system for an experimental +fusion device is, however, not trivial, and the process can be greatly aided by computer simulations +of how a proposed setup would work in the given environment. This paper presents such analysis +utilizing the RENATE-OD synthetic diagnostic code for a proposed alkali BES system on the HSX +stellarator. HSX is a device featuring an unusual operating regime in the world of fusion devices +due to the low ion temperature and low plasma density. It was found that BES shows unusual +tendencies in these conditions. The relation between beam energy and plasma penetration in low- +ion-temperature plasma, together with unique emission features facilitated by low-density plasma, +and the underlying reasons behind these features are explored in this paper. +I. +INTRODUCTION +In the current development stage of fusion technology, +plasma diagnostic tools are still much needed to sup- +port the validation of various theories and verification +of engineering solutions. +Active diagnostic techniques, +when some kind of probing of the plasma is carried out, +are especially attractive due to their localized measure- +ments. +Such a system is beam emission spectroscopy +(BES), used in several experimental devices, including +JET [1], ASDEX Upgrade [2], EAST [3], DIII-D [4], +and even small devices like COMPASS [5]. This tech- +nique probes the plasma with a neutral atomic beam, +resulting in the release of light due to interactions with +the energetic plasma particles. The wavelength of this +light is characteristic to the beam material, which can be +one of the hydrogen isotopes in larger beams, or alkali +metals (lithium, sodium) in the small diagnostic beams. +The intensity of the emitted light is highly dependent on +the plasma density, which combined with the ability of +continuous operation makes the technique ideal for spa- +tially and temporally resolved density fluctuation mea- +surements. This is especially true for alkali diagnostic +beams, with the added benefit of the emission spectrum +being well distinguishable from the background. The lim- +itation of this type of BES is the generally short, few cen- +timeters long penetration into the plasma, but the region +spanned by this limitation is still interesting in terms of +transport and magnetohydrodynamic phenomena. +The construction of a BES system requires careful de- +sign due to geometric restrictions and the desire to max- +imize the collectable photon flux [6]. As an aid for this +problem, one can utilize BES simulation codes designed +to produce synthetic measurements under the specific +plasma conditions of the device in question. Based on +the data of such simulations it is possible to analyze +the expected performance of the diagnostic system, help- +ing the engineers to balance between design trade-offs. +RENATE-OD [7] (Rate Equations for Neutral Alkali- +beam TEchnique - Open Development) is an open-source +Python package developed for this purpose. This code +solves rate equations to calculate the population of each +atomic level considered in the calculations, from which +the emissivity of the beam is acquired through the spon- +taneous transition rate for the observed spectral line. +With the ability to accurately predict the performance +of a BES system under design, it is possible to dynami- +cally test various injection and observation configurations +for optimal operation. +RENATE-OD and its precursor, RENATE [8], has al- +ready been utilized in multiple projects regarding BES +systems. The code’s capabilities have been validated with +KSTAR measurements [9, 10], it was the main tool for +a feasibility study of a BES system for JT60-SA [11], +and aided the design of lithium beam diagnostics on +EAST [12]. Recently, we performed a feasibility study for +the HSX (Helically Symmetric eXperiment) stellarator. +This device is operated by the Electrical and Computer +Engineering department of the University of Wisconsin- +Madison, with the aim of investigating transport, tur- +bulence, and confinement in a quasi-helically symmetric +magnetic field [13]. It is a small device with average ma- +jor and minor radii of 1.2 m and 0.15 m, respectively. The +magnetic field is produced by copper coils, allowing dis- +charges up to only 100 ms long, and a maximum on-axis +magnetic field of 1.25 T. The plasma density achievable in +the device is on the order of a few times 1018 m−3, which +is relatively low compared to other fusion-related plasma +experiments. As a consequence, the electrons heated by +electron cyclotron resonance are poorly coupled to the +ions, resulting in an electron and ion population with dis- +parate temperatures. Throughout this paper, we treated +the ion temperature as constant at 50 eV throughout the +machine, while the electron temperature was prescribed +with a peaked profile reaching a maximum of 2.5 keV +[14]. +As of now, the device does not have a BES system, +therefore we performed a feasibility study to explore how +the technique would perform under such conditions. Part +of this process was to simulate the evolution of lithium + +2 +(Li) and sodium (Na) beams across low-density and low- +ion-temperature plasma profiles as described before. We +observed that both the beam density and emissivity can +behave unusually under these circumstances. +Notably, +a beam with low energy could be less attenuated than +a high-energy beam, and changes in the electron tem- +perature are more visible in the emission compared to +measurements in high-density plasma. The reasons be- +hind these phenomena are explored in this work. +We +also note that these plasma conditions are not exclusive +to HSX, since they can be regularly found in the diver- +tor region of larger fusion devices. Measurement of these +regions by BES is an unexplored area, which warrants +further efforts for investigation. +The basic methodology of the calculations performed +by RENATE-OD are presented in Chapter II, then in +Chapter III the unusual effects found in beam attenua- +tion and emission are presented together with their ex- +planation. Finally, in Chapter IV the findings and their +relevance are summarized. +II. +CALCULATIONS +To simulate the density and emission of an atomic +beam along its path, RENATE-OD solves the rate equa- +tions describing the population of the valence electrons +of the beam atoms on the most populated atomic levels. +In the case of alkali atoms, the considered levels are the +l-resolved states reachable by the valence electron up to +4.5 eV in the case of lithium, which includes 2s, 2p, 3s, +3p, 3d, 4s, 4p, 4d, 4f, and up to 4.1 eV for sodium, in- +cluding 3s, 3p, 3d, 4s, 4p, 4d, 4f, 5s. The populations +of the levels evolve due to interactions with the plasma +components. The simulated light emission is governed by +collisional excitation to the higher energy levels followed +by spontaneous emission. The attenuation of the beam +is contributed to the beam atoms becoming charged, and +therefore redirected by the perpendicular magnetic field +component. This can happen through collisional ioniza- +tion or charge exchange with plasma ions, and we refer +to the sum of these processes as electron loss. The simu- +lation does not include interaction between beam atoms +due to being negligible compared to beam-plasma inter- +actions. It is also assumed that only the valence electrons +participate in the relevant processes, and initially, all of +them are in the ground state. +The atomic levels can gain or lose electrons through +multiple channels. For example, if we denote the popu- +lation density of a particular level with ni, we can write +its losses due to excitation by electrons in the form of +�dni +dt +� +el,exc += −nine +� +i