Enhancing Low-Resource Language Reasoning via High-Resource Language Feature Transfer
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 la...
| Authors | Minju Song, Hyeon Hwang, Junhyun Lee et al. |
|---|---|
| Primary category | cs.CL |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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