Update README.md

README.md

@@ -6,22 +6,19 @@ task_categories:
 - other
 tags:
 - topic-modeling
-- llm
+- llm-evaluation
 - benchmark
+- legislation
+- wikipedia
 ---
 
 # Dataset Overview
 
-This repository contains benchmark datasets for LLM-based topic discovery and traditional topic models.  These datasets allow for comparison of different topic modeling approaches, including LLMs.  Original data source: [GitHub](https://github.com/ahoho/topics?tab=readme-ov-file#download-data)
+This repository contains benchmark datasets for evaluating Large Language Model (LLM)-based topic discovery methods and comparing them against traditional topic models.  These datasets provide a valuable resource for researchers studying topic modeling and LLM capabilities in this domain.  The work is described in the following paper: [Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of LLMs](https://arxiv.org/abs/2502.14748).  Original data source: [GitHub](https://github.com/ahoho/topics?tab=readme-ov-file#download-data)
 
-**Paper:** [LLM-based Topic Discovery](https://arxiv.org/abs/2502.14748)
+## [Bills Dataset](https://huggingface.co/datasets/zli12321/Bills)
 
-## Bills Dataset
-
-The Bills Dataset is a collection of legislative documents with 32,661 bill summaries (train) from the 110th–114th U.S. Congresses, categorized into 21 top-level and 112 secondary-level topics.
-
-- **Train Split**: 32.7K summaries
-- **Test Split**: 15.2K summaries
+The Bills Dataset is a collection of legislative documents containing 32,661 bill summaries (train) from the 110th–114th U.S. Congresses, categorized into 21 top-level and 112 secondary-level topics.  A test split of 15.2K summaries is also included.
 
 ### Loading the Bills Dataset
 ```python
@@ -32,22 +29,20 @@ train_dataset = load_dataset('zli12321/Bills', split='train')
 test_dataset = load_dataset('zli12321/Bills', split='test')
 ```
 
-## Wiki Dataset
-
-The Wiki dataset consists of 14,290 articles spanning 15 high-level and 45 mid-level topics, including widely recognized public topics such as music and anime.
+## [Wiki Dataset](https://huggingface.co/datasets/zli12321/Wiki) 
 
-- **Train Split**: 14.3K summaries
-- **Test Split**: 8.02K summaries
+The [Wiki](https://huggingface.co/datasets/zli12321/Wiki) dataset consists of 14,290 articles spanning 15 high-level and 45 mid-level topics, including widely recognized public topics such as music and anime. A test split of 8.02K summaries is included.
 
-## Synthetic Science Fiction (In internal clearance process)
+## [Synthetic Science Fiction](https://huggingface.co/datasets/zli12321/Scifi4TopicModel)
+The [Science fiction](https://huggingface.co/datasets/zli12321/Scifi4TopicModel) dataset is generated from LLaMA-3-70B, with two themes for each story, and a mood.
 
 ## User Interface
 We are currently trying to make a general app that incorporates different neural topic models, LDA, and LLMs to data analysis. The goal of the app is to enable easy data visualization and data analysis for social scientists. The expected release date is the end of 2025.
 
+Please cite the relevant papers below if you find the data useful.  Do not hesitate to create an issue or email us if you have problems!
 
-**Please cite:**
 
-If you find the data and papers useful, please cite accordingly.  See below for relevant citations based on your use case.
+**Citation:**
 
 If you find LLM-based topic generation has hallucination or instability, and coherence not applicable to LLM-based topic models:
 ```
@@ -62,7 +57,6 @@ If you find LLM-based topic generation has hallucination or instability, and coh
 }
 ```
 
-
 If you use the human annotations or preprocessing:
 ```
 @inproceedings{li-etal-2024-improving,
@@ -119,4 +113,4 @@ If you evaluate ground-truth evaluations or stability:
     doi = "10.18653/v1/2022.findings-emnlp.390",
     pages = "5321--5344",
 }
-```
+```