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Found 6 papers, 0 papers with code
PAQA: Toward ProActive Open-Retrieval Question Answering
Conversational systems have made significant progress in generating natural language responses.
Navigating Uncertainty: Optimizing API Dependency for Hallucination Reduction in Closed-Book Question Answering
Our model directly provides answers for $78. 2\%$ of the known queries and opts to search for $77. 2\%$ of the unknown ones.
Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search
The only two datasets known to us that contain both document relevance judgments and the associated clarification interactions are Qulac and ClariQ.
CIRCLE: Multi-Turn Query Clarifications with Reinforcement Learning
In this paper, we introduce CIRCLE, a generative model for multi-turn query Clarifications wIth ReinforCement LEarning that leverages multi-turn interactions through a user simulation framework.
Interactive Query Clarification and Refinement via User Simulation
When users initiate search sessions, their queries are often unclear or might lack of context; this resulting in inefficient document ranking.
State of the Art of User Simulation approaches for conversational information retrieval
Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR) at the intersection of interactive IR and dialogue systems for open domain information needs.
