File size: 2,214 Bytes
e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 e471f63 358d169 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
---
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. |