3 citations · 3 across the 5 of their papers we have counts for
10 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…
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…
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…
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…