22 citations · 28 across the 9 of their papers we have counts for
10 papers
Ready to Speak: Aligning LLMs for TTS-Friendly Text Generation
Thibaut Thonet, Jos Rozen, Laurent Besacier
Current Large Language Models (LLMs) are primarily optimized for written text, often producing outputs that are grammatically correct and helpful yet poorly suited for spoken deliv…
Findings of the Third Automatic Minuting (AutoMin) Challenge
Kartik Shinde, Laurent Besacier, Ondrej Bojar +2
This paper presents the third edition of AutoMin, a shared task on automatic meeting summarization into minutes. In 2025, AutoMin featured the main task of minuting, the creation o…
FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
Thibaut Thonet, Germán Kruszewski, Jos Rozen +2
LLM-powered conversational assistants are often deployed in a one-size-fits-all manner, which fails to accommodate individual user preferences. Recently, LLM personalization -- tai…
Guaranteed Generation from Large Language Models
Minbeom Kim, Thibaut Thonet, Jos Rozen +3
As large language models (LLMs) are increasingly used across various applications, there is a growing need to control text generation to satisfy specific constraints or requirement…
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models
Thibaut Thonet, Jos Rozen, Laurent Besacier
Research on Large Language Models (LLMs) has recently witnessed an increasing interest in extending the models' context size to better capture dependencies within long documents. W…
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
Romain Deffayet, Thibaut Thonet, Dongyoon Hwang +3
Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in…
Generative Slate Recommendation with Reinforcement Learning
Romain Deffayet, Thibaut Thonet, Jean-Michel Renders +1
Recent research has employed reinforcement learning (RL) algorithms to optimize long-term user engagement in recommender systems, thereby avoiding common pitfalls such as user bore…
Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives
Romain Deffayet, Thibaut Thonet, Jean-Michel Renders +1
In this paper, we argue that the paradigm commonly adopted for offline evaluation of sequential recommender systems is unsuitable for evaluating reinforcement learning-based recomm…
SmoothI: Smooth Rank Indicators for Differentiable IR Metrics
Thibaut Thonet, Yagmur Gizem Cinar, Eric Gaussier +2
Information retrieval (IR) systems traditionally aim to maximize metrics built on rankings, such as precision or NDCG. However, the non-differentiability of the ranking operation p…
Deep -Means: Jointly clustering with -Means and learning representations
Maziar Moradi Fard, Thibaut Thonet, Eric Gaussier
We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to th…