arXiv:2606.03780v1 Announce Type: new Abstract: Causal tracing of factual recall has been studied predominantly in dense transformer language models, where interventions localize information flow to layers or feed-forward modules. Sparse mixture-of-experts (MoE) language models introduce a sharper question: when a factual prediction is mediated by a routed MoE block, which routed expert contributions matter? We formulate expert-aware causal tracing for sparse MoE language models. Using CounterFact facts, we first corrupt the model's factual preference by adding noise to subject-token embedding
The increasing scale and complexity of AI models, particularly Sparse MoE architectures, necessitate advanced debugging and interpretability techniques to understand their internal mechanisms.
This research provides a method for understanding how factual knowledge is processed in large language models, which is critical for improving model reliability, safety, and for developing more efficient and robust AI systems.
The ability to pinpoint specific 'experts' within MoE models responsible for factual recall allows for more precise interventions and fine-tuning, moving beyond layer- or module-level adjustments.
- · AI researchers
- · AI developers
- · Companies deploying large language models
- · AI safety organizations
- · Developers of black-box AI systems
- · Early-stage AI interpretability methods
Improved interpretability of Sparse MoE models leads to more effective model development and auditing.
This enhanced understanding could enable more targeted interventions to correct factual inaccuracies or undesirable biases within models.
The principle of expert-aware causal tracing may be extended to other complex modular AI architectures, fostering a new generation of transparent and controllable AI.
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