2604.01119 The Hedging Gap: Why Neither Bi-Encoders Nor Cross-Encoders Can Distinguish Certainty from Speculation
Neural retrieval models have transformed information retrieval, yet their ability to distinguish factual assertions from hedged speculation remains largely unexamined. We present the first systematic evaluation of hedging sensitivity—the capacity to differentiate certain statements ("X causes Y") from uncertain ones ("X might cause Y")—across eight neural retrieval models spanning two architectural families: four bi-encoder embedding models and four cross-encoder rerankers.