CounterFact
counterfact-7e610b9a·2 events·first seen 13d agoAliases: CounterFact
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Expert-aware causal tracing of factual recall in sparse MoE language models
A new arXiv preprint extends causal tracing methodology to sparse mixture-of-experts (MoE) language models, asking which routed experts mediate factual recall rather than just which layers or feed-forward modules. Using CounterFact facts, the authors apply noise-corruption and clean-patch interventions to Qwen3-30B-A3B-Base and Mixtral-8x7B-v0.1, finding that expert-level localization is possible in the former (a single expert at layer 44) but requires multi-expert coalition recovery in the latter. The results indicate that factual localization in MoE models is model- and protocol-dependent rather than universal.
Dual-adapter routing system improves knowledge editing precision in LLMs
A new arXiv paper introduces a route-specialized dual-adapter architecture for knowledge editing in LLMs, separating the concerns of writing edits (edit adapter) and suppressing them when irrelevant (locality adapter). A relevance router gates which adapter is applied, addressing the locality problem in memory-assisted editing. Evaluated on CounterFact, zsRE, and MQuAKE benchmarks using Llama-3.1-8B-Instruct and Qwen3-8B, the method achieves best-in-class probability-preference accuracy across all three datasets. Ablations show the gain comes from the architectural separation rather than increased parameter capacity.