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 wit
This paper offers new insights into current limitations of language models regarding quantitative reasoning, specifically their difficulty in comparing quantities with units.
Understanding these systematic errors in language models is crucial for improving their reliability and accuracy in real-world applications involving numerical and unit-based comparisons.
Current language models are shown to rely on heuristics that lead to systematic errors in quantitative comparisons, indicating a gap in their fundamental reasoning capabilities.
- · AI researchers
- · Model developers
- · Applications reliant on perfect quantitative LM reasoning
Further research will focus on developing dedicated architectures or training methods to address these specific quantitative reasoning deficits.
Improved quantitative reasoning in language models will expand their utility in scientific, engineering, and financial domains.
Enhanced numerical precision in AI could lead to more reliable autonomous systems in complex physical environments.
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