96 citations · 106 across the 7 of their papers we have counts for
9 papers
Can We Do Interpretable NLI with Graphs Based on Atomic Propositions?
Younes Boufouss, Luc Pommeret, Thomas Gerald +2
While Large Language Model (LLM)-based Natural Language Inference (NLI) systems achieve high accuracy, their decision-making processes lack auditable structures. This paper explore…
LLM-based Atomic Propositions help weak extractors: Evaluation of a Propositioner for triplet extraction
Luc Pommeret, Thomas Gerald, Patrick Paroubek +3
Knowledge Graph construction from natural language requires extracting structured triplets from complex, information-dense sentences. In this paper, we investigate if the decomposi…
Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification
Pierre Lepagnol, Thomas Gerald, Sahar Ghannay +2
This study is part of the debate on the efficiency of large versus small language models for text classification by prompting.We assess the performance of small language models in…
CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval
Nam Le Hai, Thomas Gerald, Thibault Formal +3
Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history. Most of the previou…
Continual Learning of Long Topic Sequences in Neural Information Retrieval
Thomas Gerald, Laure Soulier
In information retrieval (IR) systems, trends and users' interests may change over time, altering either the distribution of requests or contents to be recommended. Since neural ra…
Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs
Hanane Djeddal, Thomas Gerald, Laure Soulier +2
In this work, our aim is to provide a structured answer in natural language to a complex information need. Particularly, we envision using generative models from the perspective of…