Papers with arithmetic computation
From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations (2025.emnlp-main)
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Benlu Wang, Iris Xia, Yifan Zhang, Junda Wang, null Feiyun Ouyang, Shuo Han, Arman Cohan, Hong Yu, Zonghai Yao
| Challenge: | Existing benchmarks assess only the final answer with a wide numerical tolerance, overlooking systematic reasoning failures and potentially causing serious clinical misjudgments. |
| Approach: | They propose a new step-by-step evaluation pipeline that assesses formula selection, entity extraction, and arithmetic computation. |
| Outcome: | The proposed method improves the accuracy of large language models on medical benchmarks from 16.35% to 53.19%. |
Question Directed Graph Attention Network for Numerical Reasoning over Text (2020.emnlp-main)
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Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu
| Challenge: | Numerical reasoning requires both natural language understanding and arithmetic computation. |
| Approach: | They propose a graph representation for the context of the passage and question needed for numerical reasoning. |
| Outcome: | The proposed model achieves remarkable results in benchmark datasets such as DROP. |
Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are often evaluated on math word problems . however, such metrics conflate two distinct sub-skills: abstract formulation and arithmetic computation. |
| Approach: | They propose to use Final-answer-based metrics to evaluate large language models on math word problems to conflate two distinct sub-skills: abstract formulation and arithmetic computation. |
| Outcome: | The proposed model performance is bottlenecked by arithmetic computation and not abstract formulation, the study shows. |
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems (2023.emnlp-main)
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| Challenge: | Existing language models are notoriously inclined to make factual errors in tasks requiring arithmetic computation. |
| Approach: | They propose to combine existing chain-of-thought datasets into a unified format that can be used to train and evaluate open-source calculator-using models. |
| Outcome: | The proposed model doubles the accuracy of generating correct results compared to baseline models. |
How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts (2026.findings-acl)
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| Challenge: | Existing evaluations of Large Language Models for clinical numerical reasoning provide limited operation-level coverage and limited robustness of numerical understanding across clinical note formats. |
| Approach: | They propose a benchmarking tool that evaluates four main types of clinical numeracy . they present longitudinal MIMIC-IV vital-sign records in three semantically equivalent representations . |
| Outcome: | The proposed benchmark evaluates four main types of clinical numeracy: value retrieval, arithmetic computation, relational comparison, and aggregation. |
The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It (2025.emnlp-main)
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| Challenge: | Existing studies have explored methods to enhance self-correction in large language models, but little attention has been given to understanding the models’ internal mechanisms underlying error detection. |
| Approach: | They propose to use a large language model to analyze arithmetic errors in four smaller-sized LLMs and identify their internal mechanisms. |
| Outcome: | The proposed models heavily rely on consistency headstextemdashattention heads that assess surface-level alignment of numerical values in arithmetic solutions. |