Papers › ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance...

ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering

7 Oct 2022arXiv:2210.03849archive 2025-07-28

Zhiyu Chen, Shiyang Li, Charese Smiley, Zhiqiang Ma, Sameena Shah, William Yang Wang

With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. We propose a new large-scale dataset, ConvFinQA, aiming to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations. We conduct comprehensive experiments and analyses with both the neural symbolic methods and the prompting-based methods, to provide insights into the reasoning mechanisms of these two divisions. We believe our new dataset should serve as a valuable resource to push forward the exploration of real-world, complex reasoning tasks as the next research focus. Our dataset and code is publicly available at https://github.com/czyssrs/ConvFinQA.

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prog_token_to_indices czyssrs/convfinqa/code/finqanet_generator/finqa_utils.py official repository ran MIT (permissive) · 21ff8e9f6a35119e · report
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Tasks

Conversational Question AnsweringQuestion Answering

Datasets

Introduced by this paper, per the archive.

ConvFinQA

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Conversational Question Answering ConvFinQA FinQANet (RoBERTa-large) Execution Accuracy 68.90 #2 of 2 Archive leaderboard report
Conversational Question Answering ConvFinQA FinQANet (RoBERTa-large) Program Accuracy 68.24 #2 of 2 Archive leaderboard report
Question Answering ConvFinQA FinQANet (RoBERTa-large) Execution Accuracy 68.9 #2 of 3 Archive leaderboard report

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