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This research investigates how large language models handle quantitative comparisons involving units, revealing they rely on heuristic shortcuts rather than precise unit conversion near decision boundaries. The findings provide critical insight into the internal reasoning mechanisms of LMs and highlight specific failure modes in numerical cognition that could impact reliability in scientific or engineering applications.
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Language Models Compare Quantities Using Number-specific and Unit-specific Heuristics

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arXiv:2606.03982v2 Announce Type: replace Abstract: Quantities with measurement units, such as 110 cm and 1.2 m, require language models (LMs) to combine a numeral with a symbolic unit scale. Here, we study how LMs compare such quantities in controlled settings spanning several unit systems. We find that accuracy degrades near the comparison boundary, where small changes in value determine the correct answer. The resulting errors are systematic: linear surrogate models predict LM preferences from numerical-difference and unit-scale-difference cues, and causal interventions on subspaces aligned with these variables shift model's output. The results suggest that LMs compare quantities through a bag of heuristics over numerals and units, rather than first converting both expressions to an exact shared-scale representation.

Abstract:Quantities with measurement units, such as 110 cm and 1.2 m, require language models (LMs) to combine a numeral with a symbolic unit scale. Here, we study how LMs compare such quantities in controlled settings spanning several unit systems. We find that accuracy degrades near the comparison boundary, where small changes in value determine the correct answer. The resulting errors are systematic: linear surrogate models predict LM preferences from numerical-difference and unit-scale-difference cues, and causal interventions on subspaces aligned with these variables shift model's output. The results suggest that LMs compare quantities through a bag of heuristics over numerals and units, rather than first converting both expressions to an exact shared-scale representation.

Computation and Language (cs.CL)

arXiv:2606.03982 [cs.CL]

(or arXiv:2606.03982v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.03982 Focus to learn more arXiv-issued DOI via DataCite

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