Request access to Fino1 eval · DocMath-Eval CompLong (testmini)

This Fino1 task is released by The Fin AI for research. Access is granted automatically after you complete this short form.

By accessing this dataset you agree to the license of the original source (MIT License) and to cite the Fino1 paper and the original dataset in any resulting publication.

Log in or Sign Up to review the conditions and access this dataset content.

Fino1 eval · DocMath-Eval CompLong (testmini)

📄 Paper · 💻 Code · 🏆 Leaderboard · 🌐 The Fin AI

Part of Fino1 — Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance (arXiv:2502.08127).

Shown as DM-Complong on the Open FinLLM Reasoning Leaderboard.

Task numerical reasoning over long, complex financial documents
Original dataset DocMath-Eval (testmini, CompLong subset)
Source license MIT License
Language en

Quick Start

from datasets import load_dataset

ds = load_dataset("TheFinAI/en-dm-complong", split="test")
print(ds[0])

Example prompt

You are a financial expert, you are supposed to answer the given question based on the provided financial document context. You need to first think through the problem step by step, documenting each necessary step. Then you are required to conclude your response with the final answer in your last sentence as 'Therefore, the answer is {final answer}'. The final answer should be a numeric value.
###…

Dataset Structure

Split Rows
test 300
Field Description
query Full instruction prompt given to the model
answer Gold answer / label text
id Example id
text Raw input text (without instruction)

License

The original data is released under MIT License (source license as listed for DocMath-Eval in the FinCoT card).

Citation

Please cite Fino1 and the original dataset (DocMath-Eval (testmini, CompLong subset)):

@misc{qian2025fino1transferabilityreasoningenhancedllms,
      title={Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance},
      author={Lingfei Qian and Weipeng Zhou and Yan Wang and Xueqing Peng and Han Yi and Yilun Zhao and Jimin Huang and Qianqian Xie and Jian-yun Nie},
      year={2025},
      eprint={2502.08127},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.08127},
}
Downloads last month
1,108

Space using TheFinAI/en-dm-complong 1

Collection including TheFinAI/en-dm-complong

Paper for TheFinAI/en-dm-complong