Published on June 29, 2026, the item describes SHIFT, which adds learnable gates to FFN activations to balance retrieved context and parametric knowledge when they conflict.
Published on June 29, 2026, the item presents SHIFT, an internal intervention in language models. The proposal adds learnable gates to feed-forward network (FFN) activations, aiming to help a model balance evidence retrieved by RAG systems with its parametric knowledge when the two conflict.
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