Datasets:

zli12321
/

Bills

@@ -1,22 +1,25 @@
 ---
 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
@@ -26,14 +29,14 @@ 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:
 ```
@@ -104,4 +107,4 @@ If you evaluate ground-truth evaluations or stability:
     doi = "10.18653/v1/2022.findings-emnlp.390",
     pages = "5321--5344",
 }
-```

 ---
 license: apache-2.0
+task_categories:
+- other
+tags:
+- topic-modeling
+- llm-evaluation
+- benchmark
+- legislation
+- wikipedia
 ---
 # Dataset Overview
+This repository contains benchmark datasets for evaluating Large Language Model (LLM)-based topic discovery methods and comparing them against traditional topic models.  These datasets provide a valuable resource for researchers studying topic modeling and LLM capabilities in this domain.  The work is described in the following paper: [Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of LLMs](https://arxiv.org/abs/2502.14748).  Original data source: [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 containing 32,661 bill summaries (train) from the 110th–114th U.S. Congresses, categorized into 21 top-level and 112 secondary-level topics.  A test split of 15.2K summaries is also included.
 ### Loading the Bills Dataset
+```python
 from datasets import load_dataset
 # Load the train and test splits
 ## [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. A test split of 8.02K summaries is included.
 ## Synthetic Science Fiction (Pending internal clearance process)
+Please cite the relevant papers below if you find the data useful.  Do not hesitate to create an issue or email us if you have problems!
+**Citation:**
 If you find LLM-based topic generation has hallucination or instability, and coherence not applicable to LLM-based topic models:
 ```
     doi = "10.18653/v1/2022.findings-emnlp.390",
     pages = "5321--5344",
 }
+```