25 citations · 133 across the 34 of their papers we have counts for
36 papers
Probing Language Models on Their Knowledge Source
Zineddine Tighidet, Andrea Mogini, Jiali Mei +2
Large Language Models (LLMs) often encounter conflicts between their learned, internal (parametric knowledge, PK) and external knowledge provided during inference (contextual knowl…
MEXMA: Token-level objectives improve sentence representations
João Maria Janeiro, Benjamin Piwowarski, Patrick Gallinari +1
Current pre-trained cross-lingual sentence encoders approaches use sentence-level objectives only. This can lead to loss of information, especially for tokens, which then degrades…
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…
Training Table Question Answering via SQL Query Decomposition
Raphaël Mouravieff, Benjamin Piwowarski, Sylvain Lamprier
Table Question-Answering involves both understanding the natural language query and grounding it in the context of the input table to extract the relevant information. In this cont…
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…