Update README.md

README.md

@@ -1,3 +1,107 @@
----
-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+
+# Dataset Overview
+
+This repository contains benchmark datasets for LLM-based topic discovery and traditional topic models. Original [GitHub](https://github.com/ahoho/topics?tab=readme-ov-file#download-data)
+
+## [Bills Dataset](https://huggingface.co/datasets/zli12321/Bills)
+
+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](https://huggingface.co/datasets/zli12321/Wiki) 
+
+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 us if you find the data and the papers useful, and do not hesitate to create an issue or email us if you have problems!
+
+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",
+}
+```