5 citations · 24 across the 16 of their papers we have counts for
14 papers · 1 filter
Improving the Efficiency and Effectiveness of LLM Knowledge Distillation for Conversational Search
Stan Fris, Jan Hutter, Jan Henrik Bertrand +2
Conversational Search (CS) considers retrieval of relevant documents based on conversational context. Large Language Models (LLMs) have significantly enhanced CS by enabling effect…
Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval
Kidist Amde Mekonnen, Yongkang Li, Yubao Tang +2
Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam search, they are vulnerable to…
On the Challenges and Opportunities of Learned Sparse Retrieval for Code
Simon Lupart, Maxime Louis, Thibault Formal +2
Retrieval over large codebases is a key component of modern LLM-based software engineering systems. Existing approaches predominantly rely on dense embedding models, while learned…
Investigating LLM Variability in Personalized Conversational Information Retrieval
Simon Lupart, Daniël van Dijk, Eric Langezaal +2
Personalized Conversational Information Retrieval (CIR) has seen rapid progress in recent years, driven by the development of Large Language Models (LLMs). Personalized CIR aims to…
Unsupervised Corpus Poisoning Attacks in Continuous Space for Dense Retrieval
Yongkang Li, Panagiotis Eustratiadis, Simon Lupart +1
This paper concerns corpus poisoning attacks in dense information retrieval, where an adversary attempts to compromise the ranking performance of a search algorithm by injecting a…
Conversational Gold: Evaluating Personalized Conversational Search System using Gold Nuggets
Zahra Abbasiantaeb, Simon Lupart, Leif Azzopardi +2
The rise of personalized conversational search systems has been driven by advancements in Large Language Models (LLMs), enabling these systems to retrieve and generate answers for…
Reproducing NevIR: Negation in Neural Information Retrieval
Coen van den Elsen, Francien Barkhof, Thijmen Nijdam +2
Negation is a fundamental aspect of human communication, yet it remains a challenge for Language Models (LMs) in Information Retrieval (IR). Despite the heavy reliance of modern ne…
IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Search
Simon Lupart, Zahra Abbasiantaeb, Mohammad Aliannejadi
The Interactive Knowledge Assistant Track (iKAT) 2024 focuses on advancing conversational assistants, able to adapt their interaction and responses from personalized user knowledge…
DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search
Simon Lupart, Mohammad Aliannejadi, Evangelos Kanoulas
Conversational Search (CS) involves retrieving relevant documents from a corpus while considering the conversational context, integrating retrieval with context modeling. Recent ad…
Generating Multi-Aspect Queries for Conversational Search
Zahra Abbasiantaeb, Simon Lupart, Mohammad Aliannejadi
Conversational information seeking (CIS) systems aim to model the user's information need within the conversational context and retrieve the relevant information. One major approac…
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…
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…
A Study on FGSM Adversarial Training for Neural Retrieval
Simon Lupart, Stéphane Clinchant
Neural retrieval models have acquired significant effectiveness gains over the last few years compared to term-based methods. Nevertheless, those models may be brittle when faced t…
MS-Shift: An Analysis of MS MARCO Distribution Shifts on Neural Retrieval
Simon Lupart, Thibault Formal, Stéphane Clinchant
Pre-trained Language Models have recently emerged in Information Retrieval as providing the backbone of a new generation of neural systems that outperform traditional methods on a…