Publications de Antoine Bosselut

A Logical Fallacy-Informed Framework for Argument Generation

Boi Faltings, Robert West, Antoine Bosselut, Luca Mouchel, Debjit Paul

Despite the remarkable performance of large language models (LLMs), they still struggle with generating logically sound arguments, resulting in potential risks such as spreading misinformation. An important factor contributing to LLMs' suboptimal performan ...

Association for Computational Linguistics2025

Evaluating Morphological Compositional Generalization in Large Language Models

Antoine Bosselut, Mahammad Ismayilzada

Large language models (LLMs) have demonstrated significant progress in various natural language generation and understanding tasks. However, their linguistic generalization capabilities remain questionable, raising doubts about whether these models learn l ...

Association for Computational Linguistics2025

Exploring Defeasibility in Causal Reasoning

Boi Faltings, Antoine Bosselut, Debjit Paul, Mahammad Ismayilzada, Lazar Milikic

Defeasibility in causal reasoning implies that the causal relationship between cause and effect can be strengthened or weakened. Namely, the causal strength between cause and effect should increase or decrease with the incorporation of strengthening argume ...

2024

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes

Antoine Bosselut, Paola Mejia Domenzain, Seyed Parsa Neshaei, Tanja Christina Käser Jacober, Luca Mouchel, Jibril Albachir Frej, Tatjana Nazaretsky, Thiemo Wambsganss

Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input ...

2024

Course Recommender Systems Need to Consider the Job Market

Antoine Bosselut, Tanja Christina Käser Jacober, Syrielle Mathilde Marie Montariol, Jibril Albachir Frej

Current course recommender systems primarily leverage learner-course interactions, course content, learner preferences, and supplementary course details like instructor, institution, ratings, and reviews, to make their recommendation. However, these system ...

Association for Computing Machinery, Inc2024

Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning

Boi Faltings, Robert West, Antoine Bosselut, Debjit Paul

Large language models (LLMs) have been shown to perform better when asked to reason step-by-step before answering a question. However, it is unclear to what degree the model's final answer is faithful to the stated reasoning steps. In this paper, we perfor ...

Association for Computational Linguistics (ACL)2024

Instruction-tuned LLMs with World Knowledge are More Aligned to the Human Brain

Antoine Bosselut, Martin Schrimpf, Syrielle Mathilde Marie Montariol

Instruction-tuning is a widely adopted finetuning method that enables large language models (LLMs) to generate output that more closely resembles human responses. However, no studies have shown that instruction-tuning actually teaches LLMs to process langu ...

2024

ConGeo: Robust Cross-View Geo-Localization Across Ground View Variations

Devis Tuia, Antoine Bosselut, Li Mi, Syrielle Mathilde Marie Montariol, Javiera Francisca Castillo Navarro

Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires accommodating diverse ground images captured by users with varying orientat ...

Springer Cham2024

ConVQG: Contrastive Visual Question Generation with Multimodal Guidance

Devis Tuia, Antoine Bosselut, Li Mi, Syrielle Mathilde Marie Montariol, Javiera Francisca Castillo Navarro

Asking questions about visual environments is a crucial way for intelligent agents to understand rich multi-faceted scenes, raising the importance of Visual Question Generation (VQG) systems. Apart from being grounded to the image, existing VQG systems can ...

Association for the Advancement of Artificial Intelligence (AAAI)2024

Training Visual Language Models with Object Detection: Grounded Change Descriptions in Satellite Images

Devis Tuia, Antoine Bosselut, Syrielle Mathilde Marie Montariol, Javiera Francisca Castillo Navarro

Recently, generalist Vision Language Models (VLMs) have shown exceptional progress in tasks previously dominated by specialized computer vision models. This becomes more prevalent when visual grounding capabilities, such as the ability to reason over input ...

Institute of Electrical and Electronics Engineers Inc.2024