Golnoosh Farnadi

Site web

prakhar.ganesh@mila.quebec

Marylou Fauchard Fauchard

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Margarida Carvalho

maryfau@icloud.com

Prakhar Ganesh

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Stagiaire de recherche

saber.malekmohammadi@mila.quebec

Armin Moradi

Maîtrise recherche - UdeM

arminmoradi785@gmail.com

Maîtrise recherche - UdeM

Nicola Neophytou

Collaborateur·rice alumni - UdeM

Google Scholar

Elaheh Rahmati

Maîtrise recherche - Polytechnique

Reinhardt Sebastian

Stagiaire de recherche - McGill University

William St-Arnaud

Doctorat - UdeM

Co-superviseur⋅e :

Margarida Carvalho

Rohan Sukumaran

Maîtrise recherche - UdeM

Site web

Google Scholar

Afaf Taïk

Postdoctorat - UdeM

Jesse Thibodeau

Visiteur de recherche indépendant - HEC

Milan Van den Heuvel

Visiteur de recherche indépendant - Ghent University

Publications

Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset

Khaoula Chehbouni

Jonathan Colacco-Carr

Yash More

Jackie Ck Cheung

In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards o… (voir plus)utputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in practice, the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. This study addresses these issues by auditing the widely-used Helpful and Harmless (HH) dataset by Anthropic. Our work includes: (1) a thorough investigation of the dataset's content through both manual and automated evaluation; (2) experiments demonstrating the dataset's impact on models' safety; and (3) an analysis of the 100 most influential papers citing this dataset. Through our audit, we showcase how conceptualization failures and quality issues identified in the HH dataset can create additional harms by leading to disparate safety behaviors across demographic groups. Our findings highlight the need for more nuanced, context-sensitive approaches to safety mitigation in LLMs.

2024-11-12

ArXiv (prépublication)

Multilingual Hallucination Gaps in Large Language Models

Cl'ea Chataigner

Afaf Taïk

Large language models (LLMs) are increasingly used as alternatives to traditional search engines given their capacity to generate text that … (voir plus)resembles human language. However, this shift is concerning, as LLMs often generate hallucinations, misleading or false information that appears highly credible. In this study, we explore the phenomenon of hallucinations across multiple languages in freeform text generation, focusing on what we call multilingual hallucination gaps. These gaps reflect differences in the frequency of hallucinated answers depending on the prompt and language used. To quantify such hallucinations, we used the FactScore metric and extended its framework to a multilingual setting. We conducted experiments using LLMs from the LLaMA, Qwen, and Aya families, generating biographies in 19 languages and comparing the results to Wikipedia pages. Our results reveal variations in hallucination rates, especially between high and low resource languages, raising important questions about LLM multilingual performance and the challenges in evaluating hallucinations in multilingual freeform text generation.

2024-10-23

ArXiv (prépublication)

FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation

Rohan Sukumaran

Aarash Feizi

Adriana Romero-Sorian

2024-10-22

ArXiv (prépublication)

Hallucination Detox: Sensitive Neuron Dropout (SeND) for Large Language Model Training

Shahrad Mohammadzadeh

Juan David Guerra

Marco Bonizzato

Reihaneh Rabbany

As large language models (LLMs) become increasingly deployed across various industries, concerns regarding their reliability, particularly d… (voir plus)ue to hallucinations-outputs that are factually inaccurate or irrelevant to user input-have grown. Our research investigates the relationship between the training process and the emergence of hallucinations to address a key gap in existing research that focuses primarily on post hoc detection and mitigation strategies. Using models from the Pythia suite (70M-12B parameters) and several hallucination detection metrics, we analyze hallucination trends throughout training and explore LLM internal dynamics. We introduce SEnsitive Neuron Dropout (SeND), a novel training protocol designed to mitigate hallucinations by reducing variance during training. SeND achieves this by deterministically dropping neurons with significant variability on a dataset, referred to as Sensitive Neurons. In addition, we develop an unsupervised hallucination detection metric, Efficient EigenScore (EES), which approximates the traditional EigenScore in 2x speed. This efficient metric is integrated into our protocol, allowing SeND to be both computationally scalable and effective at reducing hallucinations. Our empirical evaluation demonstrates that our approach improves LLM reliability at test time by up to 40% compared to normal training while also providing an efficient method to improve factual accuracy when adapting LLMs to domains such as Wikipedia and Medical datasets.

2024-10-12

NeurIPS.cc/2024/Workshop/SafeGenAi (poster)

On the Implicit Relation Between Low-Rank Adaptation and Differential Privacy

Saber Malekmohammadi

A significant approach in natural language processing involves large-scale pre-training on general domain data followed by adaptation to spe… (voir plus)cific tasks or domains. As models grow in size, full fine-tuning all parameters becomes increasingly impractical. To address this, some methods for low-rank task adaptation of language models have been proposed, e.g. LoRA and FLoRA. These methods keep the pre-trained model weights fixed and incorporate trainable low-rank decomposition matrices into some layers of the transformer architecture, called adapters. This approach significantly reduces the number of trainable parameters required for downstream tasks compared to full fine-tuning all parameters. In this work, we look at low-rank adaptation from the lens of data privacy. We show theoretically that the low-rank adaptation used in LoRA and FLoRA is equivalent to injecting some random noise into the batch gradients w.r.t the adapter parameters coming from their full fine-tuning, and we quantify the variance of the injected noise. By establishing a Berry-Esseen type bound on the total variation distance between the noise distribution and a Gaussian distribution with the same variance, we show that the dynamics of LoRA and FLoRA are very close to differentially private full fine-tuning the adapters, which suggests that low-rank adaptation implicitly provides privacy w.r.t the fine-tuning data. Finally, using Johnson-Lindenstrauss lemma, we show that when augmented with gradient clipping, low-rank adaptation is almost equivalent to differentially private full fine-tuning adapters with a fixed noise scale.

2024-10-10

NeurIPS.cc/2024/Workshop/M3L (poster)

Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness

Ahmad Reza Ehyaei

Samira Samadi

2024-09-25

NeurIPS.cc/2024/Conference (poster)

Understanding the Local Geometry of Generative Model Manifolds

Ahmed Imtiaz Humayun

Ibtihel Amara

Candice Schumann

Mohammad Havaei

Deep generative models learn continuous representations of complex data manifolds using a finite number of samples during training. For a pr… (voir plus)e-trained generative model, the common way to evaluate the quality of the manifold representation learned, is by computing global metrics like Fr\'echet Inception Distance using a large number of generated and real samples. However, generative model performance is not uniform across the learned manifold, e.g., for \textit{foundation models} like Stable Diffusion generation performance can vary significantly based on the conditioning or initial noise vector being denoised. In this paper we study the relationship between the \textit{local geometry of the learned manifold} and downstream generation. Based on the theory of continuous piecewise-linear (CPWL) generators, we use three geometric descriptors - scaling (

2024-08-15

ArXiv (prépublication)

Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild

Niloofar Mireshghallah

Maria Antoniak

Yash More

Yejin Choi

Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users' AI literacy and facilitate pr… (voir plus)ivacy research for large language models (LLMs). We run an extensive, fine-grained analysis on the personal disclosures made by real users to commercial GPT models, investigating the leakage of personally identifiable and sensitive information. To understand the contexts in which users disclose to chatbots, we develop a taxonomy of tasks and sensitive topics, based on qualitative and quantitative analysis of naturally occurring conversations. We discuss these potential privacy harms and observe that: (1) personally identifiable information (PII) appears in unexpected contexts such as in translation or code editing (48% and 16% of the time, respectively) and (2) PII detection alone is insufficient to capture the sensitive topics that are common in human-chatbot interactions, such as detailed sexual preferences or specific drug use habits. We believe that these high disclosure rates are of significant importance for researchers and data curators, and we call for the design of appropriate nudging mechanisms to help users moderate their interactions.

2024-07-10

colmweb.org/COLM/2024/Conference (accepté)

Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities

Mohammad Havaei

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

Towards More Realistic Extraction Attacks: An Adversarial Perspective

Yash More

Prakhar Ganesh

2024-07-02

ArXiv (prépublication)

On The Local Geometry of Deep Generative Manifolds

Ahmed Imtiaz Humayun

Ibtihel Amara

Candice Schumann

Mohammad Havaei

In this paper, we study theoretically inspired local geometric descriptors of the data manifolds approximated by pre-trained generative mode… (voir plus)ls. The descriptors – local scaling (ψ), local rank (ν), and local complexity (δ) — characterize the uncertainty, dimensionality, and smoothness on the learned manifold, using only the network weights and architecture. We investigate and emphasize their critical role in understanding generative models. Our analysis reveals that the local geometry is intricately linked to the quality and diversity of generated outputs. Additionally, we see that the geometric properties are distinct for out-of-distribution (OOD) inputs as well as for prompts memorized by Stable Diffusion, showing the possible application of our proposed descriptors for downstream detection and assessment of pre-trained generative models.

2024-06-17

ICML.cc/2024/Workshop/GRaM (publié)

Mitigating Disparate Impact of Differential Privacy in Federated Learning through Robust Clustering

Saber Malekmohammadi

Afaf Taïk

Federated Learning (FL) is a decentralized machine learning (ML) approach that keeps data localized and often incorporates Differential Priv… (voir plus)acy (DP) to enhance privacy guarantees. Similar to previous work on DP in ML, we observed that differentially private federated learning (DPFL) introduces performance disparities, particularly affecting minority groups. Recent work has attempted to address performance fairness in vanilla FL through clustering, but this method remains sensitive and prone to errors, which are further exacerbated by the DP noise in DPFL. To fill this gap, in this paper, we propose a novel clustered DPFL algorithm designed to effectively identify clients' clusters in highly heterogeneous settings while maintaining high accuracy with DP guarantees. To this end, we propose to cluster clients based on both their model updates and training loss values. Our proposed approach also addresses the server's uncertainties in clustering clients' model updates by employing larger batch sizes along with Gaussian Mixture Model (GMM) to alleviate the impact of noise and potential clustering errors, especially in privacy-sensitive scenarios. We provide theoretical analysis of the effectiveness of our proposed approach. We also extensively evaluate our approach across diverse data distributions and privacy budgets and show its effectiveness in mitigating the disparate impact of DP in FL settings with a small computational cost.

2024-05-29

ArXiv (prépublication)