New method uses cross-lingual gaps to interpret MoE LLMs

Researchers have developed a novel framework to interpret Mixture-of-Experts (MoE) Large Language Models (LLMs) by leveraging cross-lingual inconsistencies. This method identifies specific model components, or "experts," crucial for recalling factual information in certain languages by contrasting their activation in languages where the model succeeds versus fails. The process involves querying the LLM with difficult factual questions across multiple languages to create "success" and "failure" activation buckets, followed by a statistical analysis of MoE router logits to pinpoint key experts. Experiments show that deactivating a small subset of these identified experts significantly impairs the model's ability to answer questions, demonstrating the effectiveness of this knowledge localization approach for understanding complex LLMs. AI

IMPACT Provides a new technique for understanding the internal workings of complex MoE LLMs, potentially aiding in debugging and improving model behavior.

RANK_REASON The cluster contains an academic paper detailing a new methodology for LLM interpretability. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Hugging Face
• Lucas Bandarkar
• Mixture-of-Experts LLMs

• paper
• other

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