Linear and spherical interpolation of ANCE and QRACDR parameters yields a single dense retriever that recovers ad-hoc effectiveness while retaining conversational skill and improving zero-shot generalization.
Learning Contextual Retrieval for Robust Conversational Search
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
RCEM aligns conversations to LLM-rewritten queries while preserving the base embedder's space, eliminating label requirements and improving robustness under distributional shift by up to 30%.
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Improving Ad-hoc Search Effectiveness for Conversational Information Retrieval via Model Merging
Linear and spherical interpolation of ANCE and QRACDR parameters yields a single dense retriever that recovers ad-hoc effectiveness while retaining conversational skill and improving zero-shot generalization.
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RCEM: Robust Conversational Search EMbedder in Distributional Shift
RCEM aligns conversations to LLM-rewritten queries while preserving the base embedder's space, eliminating label requirements and improving robustness under distributional shift by up to 30%.