COLM 2026 Paper: Code-switching mechanism improves multilingual LLM alignment
kchonyc · x · 2026-08-15
The paper "Gradual Code-Switching as Inference-Time Cross-Lingual Representational Alignment for LLMs" introduces Code-Switching In-Context Learning (CSICL), an inference-time mechanism to address the English-centric bias in LLM representations. Instead of abrupt translation pivots, CSICL gradually transitions reasoning from the target language to English to align inputs with the English-centric reasoning space.
Key Findings:
- Tested across 4 LLMs, 6 datasets, and 10 languages, CSICL consistently outperforms cross-lingual ICL baselines.
- Average performance gains: 6.0pp in target languages and 4.8pp in unseen languages.
- Gains are more pronounced in low-resource settings, reaching 14.7pp and 5.3pp for target and unseen languages respectively.
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