Which Language Should a Skeleton Speak? Language Choices in Multilingual Reasoning

2026-10-07

AI Summary

This study addresses the unclear mechanisms underlying skeleton language selection in multilingual mathematical reasoning and the lack of systematic research beyond English-centric settings. We propose a language-aware skeleton exploration framework that integrates greedy decoding, multi-trajectory evaluation, translation ablation, and cross-benchmark validation to systematically analyze skeleton language effects across varying model scales and linguistic conditions. Our findings reveal that the skeleton language is fundamentally a context-dependent design variable rather than a fixed optimal choice, identifying three distinct patterns of inconsistent language effects. Furthermore, we demonstrate that English skeletons confer only marginal advantages for smaller models operating in low-resource languages and are not universally optimal. This work provides new perspectives for optimizing multilingual reasoning systems by challenging the default assumption favoring English as the skeleton language.

Abstract

Skeleton-based reasoning prompting is a promising training-free approach for structuring LLM reasoning, but prior work largely assumes an English-centric setting. We propose the Language-Aware Skeleton Exploration Framework (LASEF) to study skeleton-language choice in multilingual mathematical reasoning. Across math benchmarks, model scales, and languages, we show that English skeletons yield a small positive tendency on average, most visible for smaller models and low-resource languages. However, few language-level gains remain significant after correction, and English is not universally optimal. Combining greedy decoding, multi-rollout evaluation, translation ablation, and cross-benchmark validation, we further find three patterns of skeleton-language effects: directionally consistent, evaluation- and benchmark-dependent, and asymmetric negative. These effects cannot be fully explained by generation quality alone. Overall, skeleton language is a context-dependent design variable that requires multi-level exploration. All resources are released at https://github.com/lhsstn/LASEF.

Problem

• multilingual reasoning
• skeleton-based prompting
• language choice
• large language models
• mathematical reasoning

Innovation

• Skeleton-based reasoning
• Multilingual reasoning
• Language-Aware Skeleton Exploration Framework
• Large Language Models
• Prompting