5 papers
MICE to Meet You: From Re-ranking Only to End-to-End Retrieval
Victor Morand, Yuxuan Zong, Mathias Vast +2
Late-interaction models offer state-of-the-art efficiency-effectiveness trade-offs for neural retrieval by enabling document representations to be pre-computed and efficiently inde…
Building Better Encoder-only Cross-Encoders: A Controlled Study of Training Strategies for Neural Re-ranking
Victor Morand, Mathias Vast, Basile Van Cooten +3
Cross-encoders fine-tuned from Transformer backbones remain the standard for second-stage re-ranking, and recent knowledge-distillation strategies have closed much of the gap with…
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…
On the Representations of Entities in Auto-regressive Large Language Models
Victor Morand, Josiane Mothe, Benjamin Piwowarski
Named entities are fundamental building blocks of knowledge in text, grounding factual information and structuring relationships within language. Despite their importance, it remai…
ToMMeR -- Efficient Entity Mention Detection from Large Language Models
Victor Morand, Nadi Tomeh, Josiane Mothe +1
Identifying which text spans refer to entities - mention detection - is both foundational for information extraction and a known performance bottleneck. We introduce ToMMeR, a ligh…