72 citations · 100 across the 20 of their papers we have counts for
18 papers · 1 filter
Reranking with Compressed Document Representation
Hervé Déjean, Stéphane Clinchant
Reranking, the process of refining the output of a first-stage retriever, is often considered computationally expensive, especially with Large Language Models. Borrowing from recen…
SPLATE: Sparse Late Interaction Retrieval
Thibault Formal, Stéphane Clinchant, Hervé Déjean +1
The late interaction paradigm introduced with ColBERT stands out in the neural Information Retrieval space, offering a compelling effectiveness-efficiency trade-off across many ben…
Two-Step SPLADE: Simple, Efficient and Effective Approximation of SPLADE
Carlos Lassance, Hervé Dejean, Stéphane Clinchant +1
Learned sparse models such as SPLADE have successfully shown how to incorporate the benefits of state-of-the-art neural information retrieval models into the classical inverted ind…
A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE
Hervé Déjean, Stéphane Clinchant, Thibault Formal
We present a comparative study between cross-encoder and LLMs rerankers in the context of re-ranking effective SPLADE retrievers. We conduct a large evaluation on TREC Deep Learnin…
SPLADE-v3: New baselines for SPLADE
Carlos Lassance, Hervé Déjean, Thibault Formal +1
A companion to the release of the latest version of the SPLADE library. We describe changes to the training structure and present our latest series of models -- SPLADE-v3. We compa…
Benchmarking Middle-Trained Language Models for Neural Search
Hervé Déjean, Stéphane Clinchant, Carlos Lassance +2
Middle training methods aim to bridge the gap between the Masked Language Model (MLM) pre-training and the final finetuning for retrieval. Recent models such as CoCondenser, RetroM…