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7 papers · 1 filter
Rank, Don't Generate: Statement-level Ranking for Explainable Recommendation
Ben Kabongo, Arthur Satouf, Vincent Guigue
Textual explanations, generated with large language models (LLMs), are increasingly used to justify recommendations. Yet, evaluating these explanations remains a critical challenge…
A Voronoi Cell Formulation for Principled Token Pruning in Late-Interaction Retrieval Models
Yash Kankanampati, Yuxuan Zong, Nadi Tomeh +2
Late-interaction models such as ColBERT offer competitive performance across various retrieval tasks but require storing a dense embedding for each document token, leading to a sub…
Towards Lossless Token Pruning in Late-Interaction Retrieval Models
Yuxuan Zong, Benjamin Piwowarski
Late interaction neural IR models like ColBERT offer a competitive effectiveness-efficiency trade-off across many benchmarks. However, they require a huge memory space to store the…
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…
ITEM: Improving Training and Evaluation of Message-Passing based GNNs for top-k recommendation
Yannis Karmim, Elias Ramzi, Raphaël Fournier-S'niehotta +1
Graph Neural Networks (GNNs), especially message-passing-based models, have become prominent in top-k recommendation tasks, outperforming matrix factorization models due to their a…