6 papers
Latent Terms: Dense Retrievers Contain Trivially Extractable BM25-ready Zipfian Vocabularies
Benjamin Clavié, Sean Lee, Aamir Shakir +1
We propose Latent Terms, a method revealing that models trained for dense retrieval, whether single- or multi-vector, learn representations that can trivially be decomposed into re…
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…
Reducing the Footprint of Multi-Vector Retrieval with Minimal Performance Impact via Token Pooling
Benjamin Clavié, Antoine Chaffin, Griffin Adams
Over the last few years, multi-vector retrieval methods, spearheaded by ColBERT, have become an increasingly popular approach to Neural IR. By storing representations at the token…
Towards Better Monolingual Japanese Retrievers with Multi-Vector Models
Benjamin Clavié
As language-specific training data tends to be sparsely available compared to English, document retrieval in many languages has been largely relying on multilingual models. In Japa…
rerankers: A Lightweight Python Library to Unify Ranking Methods
Benjamin Clavié
This paper presents rerankers, a Python library which provides an easy-to-use interface to the most commonly used re-ranking approaches. Re-ranking is an integral component of many…
JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resources
Benjamin Clavié
Neural Information Retrieval has advanced rapidly in high-resource languages, but progress in lower-resource ones such as Japanese has been hindered by data scarcity, among other c…