Publications

Characterization of astrocytic primary cilia in the adult mouse cortex and hippocampus

Sylvie C. Lahaie

Albert HK. Fok

Jessica M. Nicholls

Hannah Lee

Tabish A. Syed

Sabrina Chierzi

Sayuri Hatada

Naomi Egawa

Alex L. Schober

Tak Yi Mayumi Wong

Robert Royston

Kaleem Siddiqi

Yoshiyuki Kubota

Keith K. Murai

2025-10-31

Heliyon (publié)

doi.org

Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead

Jesujoba Oluwadara Alabi

Michael A. Hedderich

David Ifeoluwa Adelani

Dietrich Klakow

2025-10-31

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (publié)

doi.org

arxiv.org

Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog

Lautaro Estienne

Gabriel Ben Zenou

Nona Naderi

Jackie Chi Kit Cheung

Pablo Piantanida

As AI systems take on collaborative roles, they must reason about shared goals and beliefs-not just generate fluent language. The Rational S… (voir plus)peech Act (RSA) framework offers a principled approach to pragmatic reasoning, but existing extensions face challenges in scaling to multi-turn, collaborative scenarios. In this paper, we introduce Collaborative Rational Speech Act (CRSA), an information-theoretic (IT) extension of RSA that models multi-turn dialog by optimizing a gain function adapted from rate-distortion theory. This gain is an extension of the gain model that is maximized in the original RSA model but takes into account the scenario in which both agents in a conversation have private information and produce utterances conditioned on the dialog. We demonstrate the effectiveness of CRSA on referential games and template-based doctor-patient dialogs in the medical domain. Empirical results show that CRSA yields more consistent, interpretable, and collaborative behavior than existing baselines-paving the way for more pragmatic and socially aware language agents.

2025-10-31

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (publié)

doi.org

arxiv.org

Combining Virtual Reality and Hypnosis? A User Experience Study in Patients with Multiple Myeloma Following Stem Cell Transplantation

Jade Véronneau

Alexandra Chevestrier-Lefeuvre

Valentyn Fournier

Audrey Laurin

Rémi Caron-Trahan

Mathieu Landry

Joséphine Guiné

Odile Dubey-Harispe

Nadia Godin

Idrissi Moulay

Danny Wade

Sandie Oberoi

Caroline Arbour

Philippe Richebé

Karim Jerbi

Pierre Rainville

Richard LeBlanc

Floriane Rousseaux

David Ogez

Multiple myeloma (MM) and stem cell transplantation (SCT) significantly impact patients’ quality of life. Virtual reality with hypnosis (V… (voir plus)RH) has emerged as a promising nonpharmacological intervention to address these challenges, yet data on its acceptability and user experience remain scarce. This study assessed the acceptability and user experience of a VRH intervention among adult patients with MM who had undergone allogeneic SCT. Participants used a VRH application and rated their experience through standardized questionnaires and semistructured interviews. Quantitative data were analyzed descriptively, and qualitative data underwent descriptive content analysis. Findings indicated high patients’ satisfaction, strong perceived relevance, and low cybersickness. Qualitative analysis revealed perceived emotional and psychological benefits. VRH was deemed particularly suitable during hospitalization and treatment periods. This study shows the potential of combining virtual reality and hypnosis for MM patients following SCT. Indeed, they showed high satisfaction levels, paving the way for further studies evaluating the clinical efficacy of such interventions.

2025-10-31

Journal of Patient Experience (publié)

doi.org

Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs

Mina Taraghi

Yann Batiste Pequignot

Amin Nikanjam

Mohamed Amine Merzouk

Foutse Khomh

Organizations are increasingly adopting and adapting Large Language Models (LLMs) hosted on public repositories such as HuggingFace. Althoug… (voir plus)h these adaptations often improve performance on specialized downstream tasks, recent evidence indicates that they can also degrade a model's safety or fairness. Since different fine-tuning techniques may exert distinct effects on these critical dimensions, this study undertakes a systematic assessment of their trade-offs. Four widely used Parameter-Efficient Fine-Tuning methods, LoRA, IA3, Prompt-Tuning, and P-Tuning, are applied to four instruction-tuned model families (Meta-Llama-3-8B, Qwen2.5-7B, Mistral-7B, and Gemma-7B). In total, 235 fine-tuned variants are evaluated across eleven safety hazard categories and nine demographic fairness dimensions. The results show that adapter-based approaches (LoRA, IA3) tend to improve safety scores and are the least disruptive to fairness, retaining higher accuracy and lower bias scores. In contrast, prompt-based methods (Prompt-Tuning and P-Tuning) generally reduce safety and cause larger fairness regressions, with decreased accuracy and increased bias. Alignment shifts are strongly moderated by base model type: LLaMA remains stable, Qwen records modest gains, Gemma experiences the steepest safety decline, and Mistral, which is released without an internal moderation layer, displays the greatest variance. Improvements in safety do not necessarily translate into improvements in fairness, and no single configuration optimizes all fairness metrics simultaneously, indicating an inherent trade-off between these objectives. These findings suggest a practical guideline for safety-critical deployments: begin with a well-aligned base model, favour adapter-based PEFT, and conduct category-specific audits of both safety and fairness.

2025-10-31

ArXiv (prépublication)

doi.org

arxiv.org

Evaluating WMT 2025 Metrics Shared Task Submissions on the SSA-MTE African Challenge Set

Senyu Li

Felermino Dario Mario Ali

Jiayi Wang

Rui Sousa-Silva

Henrique Lopes Cardoso

Pontus Stenetorp

Colin Cherry

David Ifeoluwa Adelani

2025-10-31

Proceedings of the Tenth Conference on Machine Translation (publié)

doi.org

Findings of the WMT25 Shared Task on Automated Translation Evaluation Systems: Linguistic Diversity is Challenging and References Still Help

Alon Lavie

Greg Hanneman

Sweta Agrawal

Diptesh Kanojia

Chi-kiu Lo

Vilém Zouhar

Frédéric Blain

Chrysoula Zerva

Eleftherios Avramidis

Sourabh Dattatray Deoghare

Archchana Sindhujan

Jiayi Wang

David Ifeoluwa Adelani

Brian Thompson

Tom Kocmi

Markus Freitag

Daniel Deutsch

2025-10-31

Proceedings of the Tenth Conference on Machine Translation (publié)

doi.org

A "fine-cuts" approach disentangling psychopathic, autistic and alexithymic traits in their associations with affective, cognitive and motor empathy

Julia Ayache

Nikki Stevenson

Elisha Patel

Alexander Sumich

Guillaume Dumas

Nadja Heym

2025-10-31

Personality and Individual Differences (publié)

doi.org

fLSA: Learning Semantic Structures in Document Collections Using Foundation Models

Weijia Xu

Nebojsa Jojic

Nicolas Roux

Humans can learn to solve new tasks by inducing high-level strategies from example solutions to similar problems and then adapting these str… (voir plus)ategies to solve unseen problems. Can we use large language models to induce such high-level structure from example documents or solutions? We introduce fLSA, a foundation-model-based Latent Semantic Analysis method that iteratively clusters and tags document segments based on document-level contexts. These tags can be used to model the latent structure of given documents and for hierarchical sampling of new texts. Our experiments on story writing, math, and multi-step reasoning datasets demonstrate that fLSA tags are more informative in reconstructing the original texts than existing tagging methods. Moreover, when used for hierarchical sampling, fLSA tags help expand the output space in the right directions that lead to correct solutions more often than direct sampling and hierarchical sampling with existing tagging methods. Code: https://github.com/microsoft/fLSA

2025-10-31

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (publié)

doi.org

arxiv.org

3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM

Pierre-Yves Lajoie

Benjamin Ramtoula

Daniele De Martini

Giovanni Beltrame

Decentralized Collaborative Simultaneous Localization and Mapping (C-SLAM) techniques often struggle to identify map overlaps due to signifi… (voir plus)cant viewpoint variations among robots. Motivated by recent advancements in 3D foundation models, which can register images despite large viewpoint differences, we propose a robust loop closing approach that leverages these models to establish inter-robot measurements. In contrast to resource-intensive methods requiring full 3D reconstruction within a centralized map, our approach integrates foundation models into existing SLAM pipelines, yielding scalable and robust multi-robot mapping. Our contributions include: 1) integrating 3D foundation models to reliably estimate relative poses from monocular image pairs within decentralized C-SLAM; 2) introducing robust outlier mitigation techniques critical to the use of these relative poses and 3) developing specialized pose graph optimization formulations that efficiently resolve scale ambiguities. We evaluate our method against state-of-the-art approaches, demonstrating improvements in localization and mapping accuracy, alongside significant gains in computational and memory efficiency. These results highlight the potential of our approach for deployment in large-scale multi-robot scenarios.

2025-10-31

IEEE Robotics and Automation Letters (publié)

doi.org

arxiv.org

Intersectionality in Surgical Care in LMICs: A Systematic Scoping Review

Ayla Gerk

Shreenik Kundu

Elena Guadagno

Justina Seyi-Olajide

Dunya Moghul

Joaquim Bustorff-Silva

Cristina Camargo