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+# FinCDM-FinEval-KQA
+
+**Repository**: [NextGenWhu/FinCDM-FinEval-KQA](https://huggingface.co/datasets/NextGenWhu/FinCDM-FinEval-KQA)
+
+## 📖 Overview
+
+**FinCDM-FinEval-KQA** is a specialized dataset for financial knowledge-based question answering, derived from the research presented in:
+
+> ["From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models"](https://arxiv.org/abs/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**. 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.
+
+## 📊 Dataset Description
+
+The dataset is provided in **JSON format** and consists of multiple-choice questions tailored to financial knowledge. Each entry includes the following fields:
+
+- **`id`**: Unique identifier for the question.
+- **`query`**: The question text, including multiple-choice options.
+- **`answer`**: The correct option (e.g., A, B, C, or D).
+- **`choices`**: A list of option labels (e.g., ["A", "B", "C", "D"]).
+- **`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,
+  title={From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models},
+  author={},
+  journal={arXiv preprint arXiv:2508.13491},
+  year={2025},
+  url={https://arxiv.org/abs/2508.13491}
+}