# FinCDM-FinEval-KQA

**Repository**: [NextGenWhu/FinCDM-CPA-KQA](https://huggingface.co/datasets/NextGenWhu/FinCDM-CPA-KQA)

## 📖 Overview

**FinCDM-CPA-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}
}