---
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](https://github.com/ahoho/topics?tab=readme-ov-file#download-data)

**Paper:** [LLM-based Topic Discovery](https://arxiv.org/abs/2502.14748)

## 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
```python
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}, 
}
```

(Other citations omitted for brevity, but should remain in the final PR)

If you have problems, please create an issue or email the authors.