arXiv:2606.19668v1 Announce Type: new Abstract: Multilingual Large Language Models (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 state aligns closer to its source or target language
The proliferation of multilingual LLMs necessitates a deeper understanding of their language processing mechanisms, particularly with code-switching, as these models are increasingly deployed globally.
Improving the performance of multilingual LLMs in code-switched environments is crucial for their effective application in diverse linguistic contexts and for expanding their global adoption and reliability.
The diagnostic tool 'Anchor Bias' provides a new method to systematically analyze and address performance degradation in MLLMs when handling mixed-language inputs, leading to more robust models.
- · Multilingual LLM developers
- · Users in linguistically diverse regions
- · NLP researchers
- · AI service providers
- · Monolingual LLM development paradigms
- · Organizations relying solely on monolingual AI solutions
Understanding language anchoring allows for targeted improvements in multilingual LLM architectures and training methodologies.
Enhanced multilingual LLM performance in code-switching fosters greater global accessibility and adoption of AI technologies, particularly in emerging markets.
The development of highly robust multilingual AI could lead to new forms of human-computer interaction that seamlessly blend languages, potentially influencing global communication patterns.
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