Improve dataset card: add metadata, paper/GitHub links, and sample usage

README.md

@@ -1,14 +1,40 @@
+---
+license: mit
+task_categories:
+- question-answering
+language:
+- zh
+- en
+tags:
+- finance
+pretty_name: FinCDM-FinEval-KQA
+---
+
 # FinCDM-FinEval-KQA
 
-**Repository**: [NextGenWhu/FinCDM-CPA-KQA](https://huggingface.co/datasets/NextGenWhu/FinCDM-CPA-KQA)
+[**Paper**](https://huggingface.co/papers/2508.13491) | [**GitHub**](https://github.com/WHUNextGen/FinCDM)
 
 ## 📖 Overview
 
-**FinCDM-CPA-KQA** is a specialized dataset for financial knowledge-based question answering, derived from the research presented in:
+**FinCDM-FinEval-KQA** is a specialized dataset for financial knowledge-based question answering, serving as a knowledge-skill annotated version of the FinEval benchmark. It is introduced in the research:
+
+> ["From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models"](https://huggingface.co/papers/2508.13491)
+
+This dataset is designed to evaluate large language models (LLMs) on their ability to perform **financial knowledge reasoning**, **compliance assessment**, and **knowledge-based question answering**. Unlike traditional benchmarks, it provides fine-grained knowledge labels to identify specific strengths and weaknesses in model performance.
+
+## 🚀 Usage
 
-> ["From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models"](https://arxiv.org/abs/2508.13491)
+You can load this dataset using the Hugging Face `datasets` library:
 
-This dataset is designed to evaluate large language models (LLMs) on their ability to perform **financial knowledge reasoning**, **compliance assessment**, and **knowledge-based question answering**. It serves as a robust benchmark for assessing how well models understand and apply financial domain knowledge, making it valuable for both research and practical applications in finance.
+```python
+from datasets import load_dataset
+
+# Load FinEval-KQA dataset
+dataset = load_dataset("NextGenWhu/FinCDM-FinEval-KQA")
+
+# Examine the first sample
+print(dataset['train'][0])
+```
 
 ## 📊 Dataset Description
 
@@ -21,21 +47,15 @@ The dataset is provided in **JSON format** and consists of multiple-choice quest
 - **`gold`**: The 0-based index of the correct answer in the `choices` list.
 - **`text`**: The correct option with a brief explanation.
 
-## 🚀 Use Cases
-
-- **Benchmarking LLMs**: Evaluate the financial knowledge reasoning capabilities of large language models.
-- **Training QA Systems**: Develop and fine-tune question-answering systems for financial applications.
-- **Compliance and Auditing**: Support tasks related to financial compliance, risk assessment, and auditing.
-
 ## 📝 Citation
 
 If you use this dataset in your research or applications, please cite the following paper:
 
 ```bibtex
-@article{FinCDM2025,
+@article{fincdm2024,
   title={From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models},
-  author={},
+  author={Kuang, Ziyan and others},
   journal={arXiv preprint arXiv:2508.13491},
-  year={2025},
-  url={https://arxiv.org/abs/2508.13491}
-}
+  year={2024}
+}
+```