72 citations · 111 across the 20 of their papers we have counts for
21 papers
Rubric-Calibrated Preferences: Cross-Query Calibration of LLM Judgments via Item Response Theory
Fabian David Schmidt, Donato Crisostomi, Carlos Lassance +1
Rerankers decide which documents users and LLMs see, yet their standard metric, nDCG, relies on human relevance labels that are costly, sparse, noisy, and discretely graded. As rer…
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…
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…
End-to-End Retrieval with Learned Dense and Sparse Representations Using Lucene
Haonan Chen, Carlos Lassance, Jimmy Lin
The bi-encoder architecture provides a framework for understanding machine-learned retrieval models based on dense and sparse vector representations. Although these representations…
Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard
Ehsan Kamalloo, Nandan Thakur, Carlos Lassance +3
BEIR is a benchmark dataset for zero-shot evaluation of information retrieval models across 18 different domain/task combinations. In recent years, we have witnessed the growing po…
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…
The tale of two MS MARCO -- and their unfair comparisons
Carlos Lassance, Stéphane Clinchant
The MS MARCO-passage dataset has been the main large-scale dataset open to the IR community and it has fostered successfully the development of novel neural retrieval models over t…
A Static Pruning Study on Sparse Neural Retrievers
Carlos Lassance, Simon Lupart, Hervé Dejean +2
Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have been introduced recently as an efficient and effective way to perform retrieval with inverted indexes. They a…
AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation
Jheng-Hong Yang, Carlos Lassance, Rafael Sampaio de Rezende +4
This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval. While vision-language pretrained…
Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval
Jimmy Lin, David Alfonso-Hermelo, Vitor Jeronymo +8
The advent of multilingual language models has generated a resurgence of interest in cross-lingual information retrieval (CLIR), which is the task of searching documents in one lan…
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters
Vaishali Pal, Carlos Lassance, Hervé Déjean +1
Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and…
Naver Labs Europe (SPLADE) @ TREC NeuCLIR 2022
Carlos Lassance, Stéphane Clinchant
This paper describes our participation in the 2022 TREC NeuCLIR challenge. We submitted runs to two out of the three languages (Farsi and Russian), with a focus on first-stage rank…
Extending English IR methods to multi-lingual IR
Carlos Lassance
This paper describes our participation in the 2023 WSDM CUP - MIRACL challenge. Via a combination of i) document translation; ii) multilingual SPLADE and Contriever; and iii) multi…
Naver Labs Europe (SPLADE) @ TREC Deep Learning 2022
Carlos Lassance, Stéphane Clinchant
This paper describes our participation to the 2022 TREC Deep Learning challenge. We submitted runs to all four tasks, with a focus on the full retrieval passage task. The strategy…
An Experimental Study on Pretraining Transformers from Scratch for IR
Carlos Lassance, Hervé Déjean, Stéphane Clinchant
Finetuning Pretrained Language Models (PLM) for IR has been de facto the standard practice since their breakthrough effectiveness few years ago. But, is this approach well understo…
An Efficiency Study for SPLADE Models
Carlos Lassance, Stéphane Clinchant
Latency and efficiency issues are often overlooked when evaluating IR models based on Pretrained Language Models (PLMs) in reason of multiple hardware and software testing scenario…
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…
A Study on Token Pruning for ColBERT
Carlos Lassance, Maroua Maachou, Joohee Park +1
The ColBERT model has recently been proposed as an effective BERT based ranker. By adopting a late interaction mechanism, a major advantage of ColBERT is that document representati…
Graphs as Tools to Improve Deep Learning Methods
Carlos Lassance, Myriam Bontonou, Mounia Hamidouche +3
In recent years, deep neural networks (DNNs) have known an important rise in popularity. However, although they are state-of-the-art in many machine learning challenges, they still…
TLDR: Twin Learning for Dimensionality Reduction
Yannis Kalantidis, Carlos Lassance, Jon Almazan +1
Dimensionality reduction methods are unsupervised approaches which learn low-dimensional spaces where some properties of the initial space, typically the notion of "neighborhood",…