Papers with cross-lingual mixing

1 papers
Controlling Language Confusion in Multilingual LLMs (2025.acl-srw)

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Challenge: Large language models suffer from language confusion, a phenomenon in which responses are partially or entirely generated in unintended languages.
Approach: They propose a supervised fine-tuning methodology which optimizes the likelihood of correct tokens without explicitly penalizing undesired outputs such as cross-lingual mixing.
Outcome: The proposed model suppresses language-confused generation while maintaining strong language consistency even under high decoding temperatures while preserving general QA performance.

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