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cs.IR2025
Clarifying Ambiguities: on the Role of Ambiguity Types in Prompting Methods for Clarification Generation
Anfu Tang, Laure Soulier, Vincent Guigue
In information retrieval (IR), providing appropriate clarifications to better understand users' information needs is crucial for building a proactive search-oriented dialogue syste…
cs.CL2025
SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation
Song Duong, Florian Le Bronnec, Alexandre Allauzen +4
Large Language Models (LLMs), when used for conditional text generation, often produce hallucinations, i.e., information that is unfaithful or not grounded in the input context. Th…