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Sophie Xhonneux

sophie.xhonneux@mila.quebec
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e
Jian Tang
Co-superviseur⋅e
Gauthier Gidel

Publications

Deep Equilibrium Models For Algorithmic Reasoning
Sophie Xhonneux
Yu He
Andreea Deac
Jian Tang
Gauthier Gidel
In this blogpost we discuss the idea of teaching neural networks to reach fixed points when reasoning. Specifically, on the algorithmic reas… (voir plus)oning benchmark CLRS the current neural networks are told the number of reasoning steps they need. While a quick fix is to add a termination network that predicts when to stop, a much more salient inductive bias is that the neural network shouldn't change it's answer any further once the answer is correct, i.e. it should reach a fixed point. This is supported by denotational semantics, which tells us that while loops that terminate are the minimum fixed points of a function. We implement this idea with the help of deep equilibrium models and discuss several hurdles one encounters along the way. We show on several algorithms from the CLRS benchmark the partial success of this approach and the difficulty in making it work robustly across all algorithms.
2024-02-16
ICLR.cc/2024/BlogPosts (accepté)
openreview.net
openreview.net
Deep Equilibrium Models For Algorithmic Reasoning
Sophie Xhonneux
Yu He
Andreea Deac
Jian Tang
Gauthier Gidel
In this blogpost we discuss the idea of teaching neural networks to reach fixed points when reasoning. Specifically, on the algorithmic reas… (voir plus)oning benchmark CLRS the current neural networks are told the number of reasoning steps they need. While a quick fix is to add a termination network that predicts when to stop, a much more salient inductive bias is that the neural network shouldn't change it's answer any further once the answer is correct, i.e. it should reach a fixed point. This is supported by denotational semantics, which tells us that while loops that terminate are the minimum fixed points of a function. We implement this idea with the help of deep equilibrium models and discuss several hurdles one encounters along the way. We show on several algorithms from the CLRS benchmark the partial success of this approach and the difficulty in making it work robustly across all algorithms.
2024-02-16
ICLR.cc/2024/BlogPosts (publié)
openreview.net
openreview.net
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space
Leo Schwinn
David Dobre
Sophie Xhonneux
Gauthier Gidel
Stephan Günnemann
Current research in adversarial robustness of LLMs focuses on discrete input manipulations in the natural language space, which can be direc… (voir plus)tly transferred to closed-source models. However, this approach neglects the steady progression of open-source models. As open-source models advance in capability, ensuring their safety also becomes increasingly imperative. Yet, attacks tailored to open-source LLMs that exploit full model access remain largely unexplored. We address this research gap and propose the embedding space attack, which directly attacks the continuous embedding representation of input tokens. We find that embedding space attacks circumvent model alignments and trigger harmful behaviors more efficiently than discrete attacks or model fine-tuning. Furthermore, we present a novel threat model in the context of unlearning and show that embedding space attacks can extract supposedly deleted information from unlearned LLMs across multiple datasets and models. Our findings highlight embedding space attacks as an important threat model in open-source LLMs. Trigger Warning: the appendix contains LLM-generated text with violence and harassment.
2024-02-14
ArXiv (prépublication)
doi.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.
2024-02-08
ArXiv (prépublication)
doi.org
arxiv.org
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Zhaocheng Zhu
Xinyu Yuan
Mikhail Galkin
Louis-Pascal Xhonneux
Sophie Xhonneux
Ming Zhang
Maxime Gazeau
Jian Tang
openreview.net