Train Where the Retriever Is Confused
TL;DR for operators REPAIR suggests that the next improvement to a specialized retriever does not necessarily require a larger model or a much larger synthetic dataset. The paper instead concentrates training effort on queries where the current retriever has difficulty separating a known positive from its strongest competitor, identifies the concepts associated with those failures, and generates new supervision only after grounding it in external scientific evidence. ...