arXiv:2606.18453v1 Announce Type: new Abstract: Large language models (LLMs) exhibit substantial cross-lingual variation in mathematical reasoning performance, but it remains unclear whether these differences reflect language-specific parameters or a shared mechanism that manifests differently by language. We present a cross-lingual mechanistic analysis of mathematical reasoning in LLMs, enabling us to localize and compare model parameters that support mathematical reasoning across languages. We find that the extracted math-associated parameters exhibit partial cross-lingual overlap, with the
This paper leverages recent advancements in mechanistic interpretability to analyze LLM behavior, a capability that is rapidly maturing.
Understanding whether mathematical reasoning in LLMs uses shared or language-specific parameters informs how cross-lingual models should be designed and optimized, impacting their global utility and efficiency.
The findings suggest that a partially shared mechanism exists, which could lead to more efficient multilingual model development and better transfer learning for mathematical capabilities.
- · Multilingual LLM developers
- · AI research institutions
- · Global technology companies
- · Education technology
- · Monolingual model builders
- · Inefficient resource allocators in AI R&D
Research into designing more efficient, language-agnostic mathematical reasoning modules for LLMs will accelerate.
This could lead to a reduction in computational costs for training and deploying multilingual LLMs with strong mathematical abilities.
Improved cross-lingual mathematical reasoning could accelerate scientific collaboration and problem-solving across diverse linguistic communities.
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Read at arXiv cs.CL