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This paper introduces a method for localizing knowledge within Mixture-of-Experts (MoE) LLMs by exploiting cross-lingual inconsistencies in factual recall. The approach contrasts router logits for languages where the model succeeds versus fails in answering factual questions, identifying key experts involved in specific knowledge domains. By deactivating a small subset (around 20 out of 6000) of these identified experts, the authors demonstrate a significant drop (over 40%) in the model's ability to answer correctly, validating the method's effectiveness.
By strategically exploiting LLMs' inconsistent cross-lingual performance, this work offers a surprisingly scalable way to pinpoint the specific experts responsible for storing and retrieving factual knowledge.
Modern LLMs continue to exhibit significant variance in behavior across languages, such as being able to recall factual information in some languages but not others. While typically studied as a problem to be mitigated, in this work, we propose leveraging this cross-lingual inconsistency as a tool for interpretability in mixture-of-experts (MoE) LLMs. Our knowledge localization framework contrasts routing for sets of languages where the model correctly recalls information from languages where it fails. This allows us to isolate model components that play a functional role in answering about a piece of knowledge. Our method proceeds in two stages: (1) querying the model with difficult factual questions across a diverse set of languages to generate"success"and"failure"activation buckets and then (2) applying a statistical contrastive analysis to the MoE router logits to identify experts important for knowledge. To validate the necessity of this small number of experts for answering a knowledge question, we deactivate them and re-ask the question. We find that despite only deactivating about 20 out of 6000 experts, the model no longer answers correctly in over 40% of cases. Generally, this method provides a realistic and scalable knowledge localization approach to address increasingly complex LLMs.
