cerebras/TAT-QA-Arithmetic-CoT
收藏Hugging Face2024-08-19 更新2025-04-08 收录
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---
license: cc-by-nc-4.0
---
# Dataset Information
A Chain of Thought (CoT) version of the TAT-QA arithmetic dataset (hosted at https://huggingface.co/datasets/nvidia/ChatQA-Training-Data). The dataset was synthetically generated by prompting Llama3 70B Instruct. The dataset was created as part of our work on Cerebras DocChat - a document-based conversational Q&A model. We observed that initial iterations of our model frequently made errors on arithmetic tasks (such as ConvFinQA) because it was trained on datasets such as TAT-QA where the model must create a final equation in a single shot. We found that the addition of this dataset led to a substantial boost in accuracy (+10 on ConvFinQA).
# Acknowledgement
This dataset was is a variation of the TAT-QA dataset, and was synthetically generated using Llama 3 70B Instruct.
```
@inproceedings{zhu2021tat,
title={TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance},
author={Zhu, Fengbin and Lei, Wenqiang and Huang, Youcheng and Wang, Chao and Zhang, Shuo and Lv, Jiancheng and Feng, Fuli and Chua, Tat-Seng},
booktitle={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year={2021}
}
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
```
许可证:CC-BY-NC-4.0
# 数据集信息
本数据集为TAT-QA算术数据集的思维链(Chain of Thought, CoT)版本,托管于https://huggingface.co/datasets/nvidia/ChatQA-Training-Data。该数据集通过对Llama3 70B Instruct进行提示式生成构建而成,是我们针对Cerebras DocChat——一款基于文档的对话式问答模型——的研究工作的一部分。我们发现,模型的初始迭代版本在算术类任务(如ConvFinQA)上频繁出错,原因在于其训练所用的TAT-QA等数据集要求模型一次性生成最终计算式。实验表明,加入本数据集后,模型在相关任务上的准确率得到了显著提升(在ConvFinQA任务上提升了10个百分点)。
# 致谢
本数据集是TAT-QA数据集的衍生版本,通过Llama 3 70B Instruct合成生成。
@inproceedings{zhu2021tat,
title={TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance},
author={Zhu, Fengbin and Lei, Wenqiang and Huang, Youcheng and Wang, Chao and Zhang, Shuo and Lv, Jiancheng and Feng, Fuli and Chua, Tat-Seng},
booktitle={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year={2021}
}
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
提供机构:
cerebras



