| --- |
| pretty_name: FinBen · ConvFinQA |
| license: mit |
| language: |
| - en |
| task_categories: |
| - question-answering |
| - table-question-answering |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - extended |
| tags: |
| - finance |
| - benchmark |
| - thefinai |
| - finben |
| - flare |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: query |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: turn |
| dtype: int64 |
| - name: dialogue_id |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 44382083 |
| num_examples: 8891 |
| - name: valid |
| num_bytes: 11171617 |
| num_examples: 2213 |
| - name: test |
| num_bytes: 7116753 |
| num_examples: 1490 |
| download_size: 11803908 |
| dataset_size: 62670453 |
| extra_gated_heading: Request access to FinBen · ConvFinQA |
| extra_gated_description: This FinBen task is released by The Fin AI for research. Access is granted automatically after you complete this short form. |
| extra_gated_button_content: Agree and access |
| extra_gated_prompt: By accessing this dataset you agree to the license of the original source (MIT License) and to cite the FinBen paper and the original dataset in any resulting publication. |
| extra_gated_fields: |
| Full name: text |
| Affiliation: text |
| Country: country |
| Intended use: |
| type: select |
| options: |
| - Research |
| - Education |
| - Commercial evaluation |
| - Other |
| I agree to the terms above and will cite the papers: checkbox |
| --- |
| |
| # FinBen · ConvFinQA |
|
|
| 📄 [Paper](https://arxiv.org/abs/2402.12659) · 💻 [Code](https://github.com/The-FinAI/FinBen) · 🏆 [Leaderboard](https://huggingface.co/spaces/TheFinAI/Open-FinLLM-Leaderboard) · 🌐 [The Fin AI](https://thefin.ai) |
|
|
| > Part of **FinBen** — *FinBen: A Holistic Financial Benchmark for Large Language Models* ([arXiv:2402.12659](https://arxiv.org/abs/2402.12659)). |
|
|
| | | | |
| |---|---| |
| | Task | multi-turn question answering | |
| | Original dataset | ConvFinQA (Chen et al., 2022) | |
| | Evaluation metric | EM Accuracy | |
| | Source license | MIT License | |
| | Language | en | |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("TheFinAI/en-convfinqa", split="test") |
| print(ds[0]) |
| ``` |
|
|
| ## Example prompt |
|
|
| ```text |
| In the context of this series of interconnected finance-related queries and the additional information provided by the pretext, table data, and posttext from a company's financial filings, please provide a response to the final question. This may require extracting information from the context and performing mathematical calculations. Please take into account the information provided in the preced… |
| ``` |
|
|
| ## Dataset Structure |
|
|
| | Split | Rows | |
| |---|---| |
| | `train` | 8,891 | |
| | `test` | 1,490 | |
| | `valid` | 2,213 | |
|
|
| | Field | Description | |
| |---|---| |
| | `id` | Example id | |
| | `query` | Full instruction prompt given to the model | |
| | `answer` | Gold answer / label text | |
| | `turn` | Turn index within the dialogue | |
| | `dialogue_id` | Dialogue id | |
|
|
| ## License |
|
|
| The original data is released under **MIT License** (FinBen paper, Table 2). |
|
|
| ## Citation |
|
|
| Please cite FinBen and the original dataset (ConvFinQA (Chen et al., 2022)): |
|
|
| ```bibtex |
| @misc{xie2024finbenholisticfinancialbenchmark, |
| title={FinBen: A Holistic Financial Benchmark for Large Language Models}, |
| author={Qianqian Xie and Weiguang Han and Zhengyu Chen and Ruoyu Xiang and Xiao Zhang and Yueru He and Mengxi Xiao and Dong Li and Yongfu Dai and Duanyu Feng and Yijing Xu and Haoqiang Kang and Ziyan Kuang and Chenhan Yuan and Kailai Yang and Zheheng Luo and Tianlin Zhang and Zhiwei Liu and Guojun Xiong and Zhiyang Deng and Yuechen Jiang and Zhiyuan Yao and Haohang Li and Yangyang Yu and Gang Hu and Jiajia Huang and Xiao-Yang Liu and Alejandro Lopez-Lira and Benyou Wang and Yanzhao Lai and Hao Wang and Min Peng and Sophia Ananiadou and Jimin Huang}, |
| year={2024}, |
| eprint={2402.12659}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2402.12659}, |
| } |
| |
| @article{chen2022convfinqa, |
| title={Convfinqa: Exploring the chain of numerical reasoning in conversational finance question answering}, |
| author={Chen, Zhiyu and Li, Shiyang and Smiley, Charese and Ma, Zhiqiang and Shah, Sameena and Wang, William Yang}, |
| journal={arXiv preprint arXiv:2210.03849}, |
| year={2022} |
| } |
| ``` |
|
|
