When Should LLMs Search? Counterfactual Supervision for Search Routing
Search-augmented language models can use external evidence to compensate for limitations in parametric knowledge, but search is not uniformly beneficial: models may call search for questions they can already answer, or rely on noisy evidence when correction, clarification, or abstention would be more appropriate. We formulate this as an instance-level search-routing problem: deciding whether search is needed to improve task success relative to a no-search execution. To derive supervision, we compare no-search and forced-search outcomes for the same question and construct an oracle over NO SEARCH, SEARCH, and UNSOLVED based on task-specific success. Using this oracle as both an evaluation ...
| Authors | Minho Kim |
|---|---|
| Primary category | cs.CL |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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