From the 1 of 8 linked papers with an AI index.
1 citations · 1 across the 3 of their papers we have counts for
8 papers
DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search
Raphaël Sourty, Antoine Chaffin, Paulo Roberto Moura Junior +1
The paper introduces an open-source pipeline for training dense and late-interaction retrieval models, builds large English and multilingual contrastive datasets, and releases two…
Seq vs Seq: An Open Suite of Paired Encoders and Decoders
Orion Weller, Kathryn Ricci, Marc Marone +3
The large language model (LLM) community focuses almost exclusively on decoder-only language models, since they are easier to use for text generation. However, a large subset of th…
ColBERT-Zero: To Pre-train Or Not To Pre-train ColBERT models
Antoine Chaffin, Luca Arnaboldi, Amélie Chatelain +1
Current state-of-the-art multi-vector models are obtained through a small Knowledge Distillation (KD) training step on top of strong single-vector models, leveraging the large-scal…
LIR: The First Workshop on Late Interaction and Multi Vector Retrieval @ ECIR 2026
Benjamin Clavié, Xianming Li, Antoine Chaffin +4
Late interaction retrieval methods, pioneered by ColBERT, have emerged as a powerful alternative to single-vector neural IR. By leveraging fine-grained, token-level representations…
PyLate: Flexible Training and Retrieval for Late Interaction Models
Antoine Chaffin, Raphaël Sourty
Neural ranking has become a cornerstone of modern information retrieval. While single vector search remains the dominant paradigm, it suffers from the shortcoming of compressing al…
BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP
Thomas Sounack, Joshua Davis, Brigitte Durieux +7
Encoder-based transformer models are central to biomedical and clinical Natural Language Processing (NLP), as their bidirectional self-attention makes them well-suited for efficien…