Publications by Syrielle Mathilde Marie Montariol

Course Recommender Systems Need to Consider the Job Market

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

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

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, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol

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, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol

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, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol

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

“Flex Tape Can't Fix That”: Bias and Misinformation in Edited Language Models

Antoine Bosselut, Syrielle Mathilde Marie Montariol

Weight-based model editing methods update the parametric knowledge of language models post-training. However, these methods can unintentionally alter unrelated parametric knowledge representations, potentially increasing the risk of harm. In this work, we ...

Association for Computational Linguistics (ACL)2024

CRAB: Assessing the Strength of Causal Relationships Between Real-World Events

Karl Aberer, Angeliki Romanou, Antoine Bosselut, Debjit Paul, Syrielle Mathilde Marie Montariol, Léo Jules Laugier

Understanding narratives requires reasoning about the cause-and-effect relationships between events mentioned in the text. While existing foundation models yield impressive results in many NLP tasks requiring reasoning, it is unclear whether they understan ...

Association for Computational Linguistics2023

CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks

Antoine Bosselut, Mahammad Ismayilzada, Debjit Paul, Syrielle Mathilde Marie Montariol

Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks. However, most of these datasets formulate commonsense reasoning challenges in artificial scenarios that ar ...

Association for Computational Linguistics2023