Knowledge Localization in Mixture-of-Experts LLMs Using Cross-Lingual Inconsistency
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 fact...
| Authors | Lucas Bandarkar, Alan Ansell, Trevor Cohn |
|---|---|
| Primary category | cs.CL |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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