Publications

Deep Learning for Data-Driven Districting-and-Routing
Arthur Ferraz
Cheikh Ahmed
Quentin Cappart
Thibaut Vidal
2024-02-07
ArXiv (prépublication)
arxiv.org
arxiv.org
In-Context Learning Can Re-learn Forbidden Tasks
Sophie Xhonneux
David Dobre
Jian Tang
Gauthier Gidel
Dhanya Sridhar
Despite significant investment into safety training, large language models (LLMs) deployed in the real world still suffer from numerous vuln… (voir plus)erabilities. One perspective on LLM safety training is that it algorithmically forbids the model from answering toxic or harmful queries. To assess the effectiveness of safety training, in this work, we study forbidden tasks, i.e., tasks the model is designed to refuse to answer. Specifically, we investigate whether in-context learning (ICL) can be used to re-learn forbidden tasks despite the explicit fine-tuning of the model to refuse them. We first examine a toy example of refusing sentiment classification to demonstrate the problem. Then, we use ICL on a model fine-tuned to refuse to summarise made-up news articles. Finally, we investigate whether ICL can undo safety training, which could represent a major security risk. For the safety task, we look at Vicuna-7B, Starling-7B, and Llama2-7B. We show that the attack works out-of-the-box on Starling-7B and Vicuna-7B but fails on Llama2-7B. Finally, we propose an ICL attack that uses the chat template tokens like a prompt injection attack to achieve a better attack success rate on Vicuna-7B and Starling-7B. Trigger Warning: the appendix contains LLM-generated text with violence, suicide, and misinformation.
2024-02-07
ArXiv (prépublication)
doi.org
arxiv.org
When is Momentum Extragradient Optimal? A Polynomial-Based Analysis
Junhyung Lyle Kim
Gauthier Gidel
Anastasios Kyrillidis
Fabian Pedregosa
The extragradient method has gained popularity due to its robust convergence properties for differentiable games. Unlike single-objective op… (voir plus)timization, game dynamics involve complex interactions reflected by the eigenvalues of the game vector field's Jacobian scattered across the complex plane. This complexity can cause the simple gradient method to diverge, even for bilinear games, while the extragradient method achieves convergence. Building on the recently proven accelerated convergence of the momentum extragradient method for bilinear games \citep{azizian2020accelerating}, we use a polynomial-based analysis to identify three distinct scenarios where this method exhibits further accelerated convergence. These scenarios encompass situations where the eigenvalues reside on the (positive) real line, lie on the real line alongside complex conjugates, or exist solely as complex conjugates. Furthermore, we derive the hyperparameters for each scenario that achieve the fastest convergence rate.
2024-02-07
TMLR (accepté)
openreview.net
openreview.net
AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments
Tianyi Yang
Tianze Yang
Andrew Liu
Na An
Jie Tang
Shaoshan Liu
Xue Liu
2024-02-05
Connection science (publié)
doi.org
arxiv.org
Geometric Scattering on Measure Spaces
Joyce Chew
Matthew Hirn
Smita Krishnaswamy
Deanna Needell
Michael Perlmutter
Holly R. Steach
Siddharth Viswanath
Hau‐Tieng Wu
2024-02-05
Applied and Computational Harmonic Analysis (publié)
doi.org
arxiv.org
Polynomial Lawvere Logic
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon D. Plotkin
2024-02-04
ArXiv (prépublication)
doi.org
arxiv.org
Toward Human-AI Alignment in Large-Scale Multi-Player Games
Sugandha Sharma
Guy Davidson
Khimya Khetarpal
Anssi Kanervisto
Udit Arora
Katja Hofmann
Ida Momennejad
Achieving human-AI alignment in complex multi-agent games is crucial for creating trustworthy AI agents that enhance gameplay. We propose a … (voir plus)method to evaluate this alignment using an interpretable task-sets framework, focusing on high-level behavioral tasks instead of low-level policies. Our approach has three components. First, we analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task space. This task space serves as a basis set for a behavior manifold capturing interpretable axes: fight-flight, explore-exploit, and solo-multi-agent. Second, we train an AI agent to play Bleeding Edge using a Generative Pretrained Causal Transformer and measure its behavior. Third, we project human and AI gameplay to the proposed behavior manifold to compare and contrast. This allows us to interpret differences in policy as higher-level behavioral concepts, e.g., we find that while human players exhibit variability in fight-flight and explore-exploit behavior, AI players tend towards uniformity. Furthermore, AI agents predominantly engage in solo play, while humans often engage in cooperative and competitive multi-agent patterns. These stark differences underscore the need for interpretable evaluation, design, and integration of AI in human-aligned applications. Our study advances the alignment discussion in AI and especially generative AI research, offering a measurable framework for interpretable human-agent alignment in multiplayer gaming.
2024-02-04
ArXiv (prépublication)
doi.org
arxiv.org
Carthago Delenda Est: Co-opetitive Indirect Information Diffusion Model for Influence Operations on Online Social Media
Jwen Fai Low
Benjamin C. M. Fung
Farkhund Iqbal
Claude Fachkha
2024-02-01
ArXiv (prépublication)
doi.org
arxiv.org
Family‐centred care interventions for children with chronic conditions: A scoping review
Andrea J. Chow
Ammar Saad
Zobaida Al‐Baldawi
Ryan Iverson
Becky Skidmore
Isabel Jordan
Nicole Pallone
Maureen Smith
Pranesh Chakraborty
Jamie Brehaut
Eyal Cohen
Sarah Dyack
Jane Gillis
Sharan Goobie
Cheryl Greenberg
Robin Hayeems
Brian Hutton
Michal Inbar-Feigenberg
Shailly Jain-Ghai
Sara Khangura … (voir 18 de plus)
Jennifer MacKenzie
John J. Mitchell
Zeinab Moazin
Stuart G. Nicholls
Amy Pender
Chitra Prasad
Andreas Schulze
Komudi Siriwardena
Rebecca N. Sparkes
Kathy N. Speechley
Sylvia Stockler
Monica Taljaard
Mari Teitelbaum
Yannis Trakadis
Clara Van Karnebeek
Jagdeep S. Walia
Kumanan Wilson
Beth K. Potter
2024-02-01
Health Expectations : An International Journal of Public Participation in Health Care and Health Policy (publié)
doi.org
LitLLM: A Toolkit for Scientific Literature Review
Shubham Agarwal
Issam Hadj Laradji
Laurent Charlin
Christopher Pal
Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work.… (voir plus) It is a tedious task which makes an automatic literature review generator appealing. Unfortunately, many existing works that generate such reviews using Large Language Models (LLMs) have significant limitations. They tend to hallucinate-generate non-actual information-and ignore the latest research they have not been trained on. To address these limitations, we propose a toolkit that operates on Retrieval Augmented Generation (RAG) principles, specialized prompting and instructing techniques with the help of LLMs. Our system first initiates a web search to retrieve relevant papers by summarizing user-provided abstracts into keywords using an off-the-shelf LLM. Authors can enhance the search by supplementing it with relevant papers or keywords, contributing to a tailored retrieval process. Second, the system re-ranks the retrieved papers based on the user-provided abstract. Finally, the related work section is generated based on the re-ranked results and the abstract. There is a substantial reduction in time and effort for literature review compared to traditional methods, establishing our toolkit as an efficient alternative. Our open-source toolkit is accessible at https://github.com/shubhamagarwal92/LitLLM and Huggingface space (https://huggingface.co/spaces/shubhamagarwal92/LitLLM) with the video demo at https://youtu.be/E2ggOZBAFw0.
2024-02-01
ArXiv (prépublication)
doi.org
arxiv.org
Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity
Antea D’Andrea
Pierpaolo Croce
Jordan O’Byrne
Karim Jerbi CoCo Lab
Annalisa Pascarella
Antonino Raffone
Vittorio Pizzella
Laura Marzetti
2024-02-01
Frontiers in Neuroscience (publié)
doi.org
Adaptation, Translation, and Validation of a Patient-Reported Experience Measure for Children and Young People for the Canadian Context
Zanib Nafees
Julia Ferreira
Elena Guadagno
Jo Wray
Agneta Anderzén-Carlsson
Dan Poenaru
2024-01-31
Journal of Pediatric Surgery (publié)
doi.org