Enhancing Low-Resource Language Reasoning via High-Resource Language Feature Transfer

2026-08-31

AI Summary

研究通过从高资源语言向低资源语言转移任务相关稀疏潜在特征，以增强低资源语言的推理能力，解决跨语言性能差异问题。

Abstract

Large language models exhibit substantial performance variation across languages, even when solving semantically equivalent tasks. Existing analyses often treat this phenomenon as an observational disparity caused by differences in pretraining data, tokenization, or benchmark coverage. We study a complementary hypothesis: high-resource languages (HRLs) may more reliably elicit latent computations useful for task-specific (i.e. mathematical) reasoning, while lower-resource languages (LRLs) may under-activate those computations despite expressing the same task. To test this hypothesis, we introduce a mechanistic intervention framework for identifying and transferring task-relevant sparse latent features across languages. Using sparse autoencoders over residual-stream activations, we isolate features enriched in successful HRL task-specific reasoning while filtering out source-language and generic-generation features. We then construct steering directions from these features and inject them during LRL inference. The resulting interventions test whether the selected features are functionally involved in the observed reasoning gap: suppressing them should impair source-language reasoning, while activating them should partially recover target-language reasoning beyond random and non-task controls. Our framework reframes some cross-lingual reasoning gaps as failures of mechanism elicitation rather than capability absence, and offers a causally testable route to feature-mediated transfer without translation, fine-tuning, or changing the user-facing language.

Problem

• low-resource language
• high-resource language
• language reasoning
• latent computation
• feature transfer

Innovation

• Sparse Latent Features
• Mechanistic Intervention Framework
• Cross-lingual Reasoning
• Feature-mediated Transfer
• Residual-stream Activations