15 citations · 29 across the 5 of their papers we have counts for
5 papers
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective
Thibault Formal, Carlos Lassance, Benjamin Piwowarski +1
Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation a…
Composite Code Sparse Autoencoders for first stage retrieval
Carlos Lassance, Thibault Formal, Stephane Clinchant
We propose a Composite Code Sparse Autoencoder (CCSA) approach for Approximate Nearest Neighbor (ANN) search of document representations based on Siamese-BERT models. In Informatio…
SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
Thibault Formal, Carlos Lassance, Benjamin Piwowarski +1
In neural Information Retrieval (IR), ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using…
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking
Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant
In neural Information Retrieval, ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using effic…
A White Box Analysis of ColBERT
Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant
Transformer-based models are nowadays state-of-the-art in ad-hoc Information Retrieval, but their behavior is far from being understood. Recent work has claimed that BERT does not…