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 across eight neural retrieval models spanning two architectural families: four bi-encoder embedding models and four cross-encoder rerankers.
Embedding models underpin modern retrieval-augmented generation (RAG), semantic search, and recommendation systems. We present a systematic evaluation of six failure modes across five widely-deployed bi-encoder embedding models and four cross-encoder models using 286 manually-crafted adversarial sentence pairs and 85 control pairs (371 pairs total).
Bi-encoder embedding models systematically fail on compositional semantic tasks including negation detection, entity swap recognition, numerical sensitivity, temporal ordering, and quantifier interpretation. Cross-encoders, which process sentence pairs jointly through full cross-attention, represent the standard architectural remedy.