4 citations · 9 across the 10 of their papers we have counts for
11 papers
MICE: Minimal Interaction Cross-Encoders for efficient Re-ranking
Mathias Vast, Victor Morand, Josiane Mothe +1
In Information Retrieval (IR), cross-encoders deliver state-of-the-art ranking effectiveness but have a high inference cost, limiting their use to second-stage re-rankers. Prior wo…
Understanding Matching Mechanisms in Cross-Encoders
Mathias Vast, Basile Van Cooten, Laure Soulier +1
Neural IR architectures, particularly cross-encoders, are highly effective models whose internal mechanisms are mostly unknown. Most works trying to explain their behavior focused…
An Evaluation Framework for Attributed Information Retrieval using Large Language Models
Hanane Djeddal, Pierre Erbacher, Raouf Toukal +4
With the growing success of Large Language models (LLMs) in information-seeking scenarios, search engines are now adopting generative approaches to provide answers along with in-li…
Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders
Mathias Vast, Basile Van Cooten, Laure Soulier +1
With the recent addition of Retrieval-Augmented Generation (RAG), the scope and importance of Information Retrieval (IR) has expanded. As a result, the importance of a deeper under…
What Makes Multimodal In-Context Learning Work?
Folco Bertini Baldassini, Mustafa Shukor, Matthieu Cord +2
Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL…
PAQA: Toward ProActive Open-Retrieval Question Answering
Pierre Erbacher, Jian-Yun Nie, Philippe Preux +1
Conversational systems have made significant progress in generating natural language responses. However, their potential as conversational search systems is currently limited due t…
Navigating Uncertainty: Optimizing API Dependency for Hallucination Reduction in Closed-Book Question Answering
Pierre Erbacher, Louis Falissar, Vincent Guigue +1
While Large Language Models (LLM) are able to accumulate and restore knowledge, they are still prone to hallucination. Especially when faced with factual questions, LLM cannot only…
Simple Domain Adaptation for Sparse Retrievers
Mathias Vast, Yuxuan Zong, Basile Van Cooten +2
In Information Retrieval, and more generally in Natural Language Processing, adapting models to specific domains is conducted through fine-tuning. Despite the successes achieved by…
Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search
Pierre Erbacher, Jian-Yun Nie, Philippe Preux +1
A peculiarity of conversational search systems is that they involve mixed-initiatives such as system-generated query clarifying questions. Evaluating those systems at a large scale…
CIRCLE: Multi-Turn Query Clarifications with Reinforcement Learning
Pierre Erbacher, Laure Soulier
Users often have trouble formulating their information needs into words on the first try when searching online. This can lead to frustration, as they may have to reformulate their…
Improving generalization in large language models by learning prefix subspaces
Louis Falissard, Vincent Guigue, Laure Soulier
This article focuses on large language models (LLMs) fine-tuning in the scarce data regime (also known as the "few-shot" learning setting). We propose a method to increase the gene…