6 papers
RAC: Retrieval-Augmented Clarification for Faithful Conversational Search
Ahmed Rayane Kebir, Vincent Guigue, Lynda Said Lhadj +1
Clarification questions help conversational search systems resolve ambiguous or underspecified user queries. While prior work has focused on fluency and alignment with user intent,…
SEval-Ex: A Statement-Level Framework for Explainable Summarization Evaluation
Tanguy Herserant, Vincent Guigue
Evaluating text summarization quality remains a critical challenge in Natural Language Processing. Current approaches face a trade-off between performance and interpretability. We…
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…
Drought forecasting using a hybrid neural architecture for integrating time series and static data
Julian Agudelo, Vincent Guigue, Cristina Manfredotti +1
Reliable forecasting is critical for early warning systems and adaptive drought management. Most previous deep learning approaches focus solely on homogeneous regions and rely on s…
Towards Lighter and Robust Evaluation for Retrieval Augmented Generation
Alex-Razvan Ispas, Charles-Elie Simon, Fabien Caspani +1
Large Language Models are prompting us to view more NLP tasks from a generative perspective. At the same time, they offer a new way of accessing information, mainly through the RAG…
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…