Improve dataset card: Add metadata, paper link, project page, and sample usage

README.md

@@ -1,4 +1,13 @@
 ---
+task_categories:
+- text-ranking
+tags:
+- creative-writing
+- llm-evaluation
+- preference-alignment
+- reward-modeling
+- benchmark
+- reddit
 dataset_info:
   features:
   - name: prompt
@@ -28,4 +37,28 @@ configs:
     path: data/train-*
 ---
 
-If you are the author of any comment in this dataset and would like it removed, please contact us and we will comply promptly.
+# LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing
+
+LitBench is the first standardized benchmark and paired dataset for reliable evaluation of creative writing generated by large language models (LLMs). It addresses the challenge of evaluating open-ended narratives, which lack ground truths. The dataset comprises a held-out test set of 2,480 debiased, human-labeled story comparisons drawn from Reddit and a 43,827-pair training corpus of human preference labels. LitBench facilitates benchmarking zero-shot LLM judges and training reward models for creative writing verification and optimization.
+
+**Paper:** [LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing](https://huggingface.co/papers/2507.00769)
+
+**Project Page (Hugging Face Collection):** https://huggingface.co/collections/SAA-Lab/litbench-68267b5da3aafe58f9e43461
+
+### Sample Usage
+
+You can load the dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("SAA-Lab/LitBench")
+
+# Access the training split
+train_dataset = dataset["train"]
+
+# Print the first example
+print(train_dataset[0])
+```
+
+If you are the author of any comment in this dataset and would like it removed, please contact us and we will comply promptly.