metadata

language:
  - en
license: apache-2.0
task_categories:
  - other
tags:
  - topic-modeling
  - llm
  - benchmark

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

Paper: LLM-based Topic Discovery

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

Loading the Bills Dataset

from datasets import load_dataset

# Load the train and test splits
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.

Train Split: 14.3K summaries
Test Split: 8.02K summaries

Synthetic Science Fiction (Pending internal clearance process)

Please cite:

If you find the data and papers useful, please cite accordingly. See below for relevant citations based on your use case.

If you find LLM-based topic generation has hallucination or instability, and coherence not applicable to LLM-based topic models:

@misc{li2025largelanguagemodelsstruggle,
      title={Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of LLMs}, 
      author={Zongxia Li and Lorena Calvo-Bartolomé and Alexander Hoyle and Paiheng Xu and Alden Dima and Juan Francisco Fung and Jordan Boyd-Graber},
      year={2025},
      eprint={2502.14748},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.14748}, 
}

If you use the human annotations or preprocessing:

@inproceedings{li-etal-2024-improving,
    title = "Improving the {TENOR} of Labeling: Re-evaluating Topic Models for Content Analysis",
    author = "Li, Zongxia  and
      Mao, Andrew  and
      Stephens, Daniel  and
      Goel, Pranav  and
      Walpole, Emily  and
      Dima, Alden  and
      Fung, Juan  and
      Boyd-Graber, Jordan",
    editor = "Graham, Yvette  and
      Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-long.51/",
    pages = "840--859"
}

If you want to use the claim coherence does not generalize to neural topic models:

@inproceedings{hoyle-etal-2021-automated,
    title = "Is Automated Topic Evaluation Broken? The Incoherence of Coherence",
    author = "Hoyle, Alexander Miserlis  and
      Goel, Pranav  and
      Hian-Cheong, Andrew and
      Peskov, Denis and
      Boyd-Graber, Jordan and
      Resnik, Philip",
    booktitle = "Advances in Neural Information Processing Systems",
    year = "2021",
    url = "https://arxiv.org/abs/2107.02173",
}

If you evaluate ground-truth evaluations or stability:

@inproceedings{hoyle-etal-2022-neural,
    title = "Are Neural Topic Models Broken?",
    author = "Hoyle, Alexander Miserlis  and
      Goel, Pranav  and
      Sarkar, Rupak  and
      Resnik, Philip",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    year = "2022",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.390",
    doi = "10.18653/v1/2022.findings-emnlp.390",
    pages = "5321--5344",
}