aiqbench analysis
This study reveals that language models often rely on a "bag of heuristics" to compare quantities with units rather than performing exact unit conversions, leading to systematic errors near comparison boundaries. The findings indicate that model outputs can be predicted by linear surrogates based on numerical and unit-scale differences, suggesting a lack of robust internal shared-scale representations. For developers, this highlights a specific vulnerability in reasoning tasks involving mixed units that may require architectural or training adjustments to improve reliability.
Language Models Compare Quantities Using Number-specific and Unit-specific Heuristics
arXiv:2606.03982v1 Announce Type: new 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:2606.03982v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.03982 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 207 words
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