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--- |
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license: apache-2.0 |
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task_categories: |
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- other |
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tags: |
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- topic-modeling |
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- llm |
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- benchmark |
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--- |
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# Dataset Overview |
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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) |
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**Paper:** [LLM-based Topic Discovery](https://arxiv.org/abs/2502.14748) |
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## Bills Dataset |
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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. |
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- **Train Split**: 32.7K summaries |
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- **Test Split**: 15.2K summaries |
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### Loading the Bills Dataset |
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```python |
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from datasets import load_dataset |
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# Load the train and test splits |
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train_dataset = load_dataset('zli12321/Bills', split='train') |
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test_dataset = load_dataset('zli12321/Bills', split='test') |
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``` |
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## Wiki Dataset |
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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. |
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- **Train Split**: 14.3K summaries |
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- **Test Split**: 8.02K summaries |
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## Synthetic Science Fiction (Pending internal clearance process) |
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**Please cite:** |
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If you find the data and papers useful, please cite accordingly. See below for relevant citations based on your use case. |
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If you find LLM-based topic generation has hallucination or instability, and coherence not applicable to LLM-based topic models: |
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``` |
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@misc{li2025largelanguagemodelsstruggle, |
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title={Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of LLMs}, |
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author={Zongxia Li and Lorena Calvo-Bartolomé and Alexander Hoyle and Paiheng Xu and Alden Dima and Juan Francisco Fung and Jordan Boyd-Graber}, |
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year={2025}, |
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eprint={2502.14748}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2502.14748}, |
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} |
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``` |
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(Other citations omitted for brevity, but should remain in the final PR) |
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If you have problems, please create an issue or email the authors. |
