DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents
On the map: LLM reasoning → Mathematical reasoning
Abstract (arXiv)
Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert domains, is still largely unexplored. This paper introduces DocMath-Eval, a comprehensive benchmark specifically designed to evaluate the numerical reasoning capabilities of LLMs in the context of understanding and analyzing specialized documents containing both text and tables. We conduct an extensive evaluation of 48 LLMs with Chain-of-Thought and Program-of-Thought prompting methods, aiming to comprehensively assess the capabilities and limitations of existing LLMs in DocMath-Eval. We found that even the current best-performing system (i.e., GPT-4o) still significantly lags behind human experts in solving complex numerical reasoning problems grounded in long contexts. We believe that DocMath-Eval can serve as a valuable benchmark for evaluating LLMs' capabilities in solving challenging numerical reasoning problems within expert domains.
Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Placed in Mathematical reasoning, one of 17 directions in LLM reasoning. Builds on 23 of its 53 references that are in the map. First posted on arXiv 16 Nov 2023.
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23 of 53 references.
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Other references · 22
- 2021Training Verifiers to Solve Math Word Problemscited by 9,743
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- 2019DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphscited by 883
- 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
- 2023WizardCoder: Empowering Code Large Language Models with Evol-Instructcited by 728
- 2019MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalismscited by 653
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- 2021A Diverse Corpus for Evaluating and Developing English Math Word Problem Solverscited by 314
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- 2023FinGPT: Large Generative Models for a Small Languagecited by 49
- 2023Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Examscited by 18
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