Papers with numerical reasoning capabilities

3 papers
LLMs Know More About Numbers than They Can Say (2026.eacl-short)

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Challenge: Large language models (LLMs) are increasingly used in mathematical, scientific, financial and engineering domains.
Approach: They probe the hidden states of several smaller open-source LLMs to find out how big they are .
Outcome: The proposed model improves verbalized accuracy by 3.22% over base models.
Exploiting Numerical-Contextual Knowledge to Improve Numerical Reasoning in Question Answering (2022.findings-naacl)

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Challenge: Existing numerical reasoning models overly rely on parametric knowledge at inference time . previous studies show that understanding numbers in text improves numerical reasoning accuracy .
Approach: They propose a numerical reasoning model that leverages parametric knowledge to alleviate this over-reliance on parametric information.
Outcome: The proposed model improves numerical reasoning accuracy and performance in DROP.
Do NLP Models Know Numbers? Probing Numeracy in Embeddings (D19-1)

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Challenge: Existing models cannot capture numeracy, but they can be useful for complex reasoning tasks.
Approach: They investigate numerical reasoning capabilities of a question-answering model . they probe token embedding methods on synthetic list maximum, number decoding, and addition tasks.
Outcome: The proposed model excels on questions that require numerical reasoning, i.e., it already captures numeracy.

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