Papers with numerical reasoning tasks
Anchoring Depends on Confidence and Post-Training in Language Models (2026.acl-short)
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| Challenge: | Existing work has demonstrated the presence of anchoring bias in large language models . Existing research does not predict when a model will be most susceptible to anchoring . |
| Approach: | They analyze anchoring bias as a function of model confidence and accuracy . they find that incorrect models resist anchoring as effectively as accurate ones . |
| Outcome: | The findings suggest that anchoring resistance is a structural property of uncertainty rather than knowledge correctness. |
Numeracy enhances the Literacy of Language Models (2021.emnlp-main)
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| Challenge: | Specialized number representations have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction. |
| Approach: | They propose to use six different number encoders to improve masked word prediction by avoiding conflating nominal and ordinal number occurrences. |
| Outcome: | The proposed representations improve masked word prediction accuracy and generalize to contexts without annotated numbers. |
BenNumEval: A Benchmark to Assess LLMs’ Numerical Reasoning Capabilities in Bengali (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel in general-purpose tasks but struggle with numerical reasoning, especially in low-resource languages like Bengali. |
| Approach: | They propose a benchmark to assess LLMs on numerical reasoning tasks in Bengali. |
| Outcome: | The proposed benchmark assesses LLMs on numerical reasoning tasks in Bengali. |
Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models (2025.findings-acl)
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Haoyang Li, Xuejia Chen, Zhanchao Xu, Darian Li, Nicole Hu, Fei Teng, Yiming Li, Luyu Qiu, Chen Jason Zhang, Li Qing, Lei Chen
| Challenge: | Existing benchmarks focus on linguistic competence or structured mathematical problem-solving, neglecting fundamental numerical reasoning required in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate numerical capabilities for large language models . they use a dataset to assess number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
| Outcome: | The proposed benchmark evaluates six fundamental numerical capabilities: number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) face challenges in reliably solving reasoning tasks, especially when solving tasks that require strict rule following. |
| Approach: | They propose a method that encodes hidden states into neurosymbolic vectors and decodes them into a neurosample vector space to enable problem-solving within a neural space. |
| Outcome: | The proposed method shows an average of 88.6% lower cross-entropy loss and 15.4 times more problems correctly solved on a suite of mathematical reasoning tasks compared to chain-of-thought prompting and supervised fine-tuning (LoRA). |