Code-Switching Reveals Language Anchoring in Multilingual LLMs
Quick Answer
This paper shows that Multilingual Large Language Models (MLLMs) struggle with Code-Switched (CS) inputs, showing performance degradation due to Anchor Bias.
Quick Take
The proposed CANVAS intervention improves Question Answering (QA) F1 scores across various MLLMs by aligning target-language hidden states with source anchors during inference.
Key Points
- Anchor Bias quantifies language anchoring in MLLMs, revealing a grammar-frame effect.
- Source-framed CS maintains source anchoring, while target-framed CS shows greater QA degradation.
- CANVAS intervention effectively recovers QA F1 scores across diverse MLLMs and CS conditions.
- Internal anchoring signals can mitigate CS inference failures in multilingual models.
Paper Resources
Source Excerpt
Multilingual (MLLMs) are increasingly expected to handle Code-Switched (CS) inputs, yet mixing languages frequently degrades performance relative to source- or target-language monolingual counterparts. To understand this degradation, we use grammar-forced CS as a controlled diagnostic setting for locating CS representations relative to their source and target counterparts. We introduce Anchor Bias, a geometric measure that quantifies language anchoring, whether a CS hidden
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