Jackie Cheung

Nadhem Benhadjali

Collaborating researcher

Meng (Caden) Cao

PhD - McGill University

Google Scholar

Aishik Chakraborty

PhD - McGill University

Khaoula Chehbouni

PhD - McGill University

Principal supervisor :

Master's Research - McGill University

Antoine Dangeard

Research Intern - McGill University

Maxime Darrin

PhD - McGill University

Co-supervisor :

PhD - McGill University

Google Scholar

Aylin Erman

PhD - McGill University

Co-supervisor :

Dan Poenaru

Ori Ernst

Postdoctorate - McGill University

Master's Research - McGill University

Google Scholar

Jules Gagnon-marchand

Master's Research - McGill University

Sienna Hsu

Research Intern - McGill University University

Steven Koniaev

Research Intern - McGill University

Zichao Li

PhD - McGill University

Principal supervisor :

Siva Reddy

Caleb Moses

PhD - McGill University

Ian Porada

PhD - McGill University

PhD - McGill University

Sina Salmannia

Undergraduate - McGill University

Cesare Spinoso-Di Piano

PhD - McGill University

Nathan Zeweniuk

Research Intern - McGill University University

Xiyuan Zou

Master's Research - McGill University

Publications

Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset

Khaoula Chehbouni

Jonathan Colaco Carr

Yash More

Golnoosh Farnadi

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… (see more)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 (preprint)

Do LLMs Build World Representations? Probing Through the Lens of State Abstraction

Zichao Li

Yanshuai Cao

2024-09-25

NeurIPS.cc/2024/Conference (poster)

When is an Embedding Model More Promising than Another?

Maxime DARRIN

Philippe Formont

Ismail Ben Ayed

Pablo Piantanida

2024-09-25

NeurIPS.cc/2024/Conference (poster)

From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards

Khaoula Chehbouni

Megha Roshan

Emmanuel Ma

Futian Andrew Wei

Afaf Taïk

Golnoosh Farnadi

2024-08-01

Findings of the Association for Computational Linguistics ACL 2024 (published)

Investigating Failures to Generalize for Coreference Resolution Models

Ian Porada

Alexandra Olteanu

Kaheer Suleman

Adam Trischler

Coreference resolution models are often evaluated on multiple datasets. Datasets vary, however, in how coreference is realized -- i.e., how … (see more)the theoretical concept of coreference is operationalized in the dataset -- due to factors such as the choice of corpora and annotation guidelines. We investigate the extent to which errors of current coreference resolution models are associated with existing differences in operationalization across datasets (OntoNotes, PreCo, and Winogrande). Specifically, we distinguish between and break down model performance into categories corresponding to several types of coreference, including coreferring generic mentions, compound modifiers, and copula predicates, among others. This break down helps us investigate how state-of-the-art models might vary in their ability to generalize across different coreference types. In our experiments, for example, models trained on OntoNotes perform poorly on generic mentions and copula predicates in PreCo. Our findings help calibrate expectations of current coreference resolution models; and, future work can explicitly account for those types of coreference that are empirically associated with poor generalization when developing models.

2024-08-01

Findings of the Association for Computational Linguistics ACL 2024 (published)

GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews

Maxime DARRIN

Ines Arous

Pablo Piantanida

Scientific peer review is essential for the quality of academic publications. However, the increasing number of paper submissions to confere… (see more)nces has strained the reviewing process. This surge poses a burden on area chairs who have to carefully read an ever-growing volume of reviews and discern each reviewer's main arguments as part of their decision process. In this paper, we introduce \sys, a summarization method designed to offer a concise yet comprehensive overview of scholarly reviews. Unlike traditional consensus-based methods, \sys extracts both common and unique opinions from the reviews. We introduce novel uniqueness scores based on the Rational Speech Act framework to identify relevant sentences in the reviews. Our method aims to provide a pragmatic glimpse into all reviews, offering a balanced perspective on their opinions. Our experimental results with both automatic metrics and human evaluation show that \sys generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in terms of automatic metrics.

2024-06-11

ArXiv (preprint)

When is an Embedding Model More Promising than Another?

Maxime DARRIN

Philippe Formont

Ismail Ben Ayed

Pablo Piantanida

Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to p… (see more)erform various downstream tasks. The evaluation of embedding models typically depends on domain-specific empirical approaches utilizing downstream tasks, primarily because of the lack of a standardized framework for comparison. However, acquiring adequately large and representative datasets for conducting these assessments is not always viable and can prove to be prohibitively expensive and time-consuming. In this paper, we present a unified approach to evaluate embedders. First, we establish theoretical foundations for comparing embedding models, drawing upon the concepts of sufficiency and informativeness. We then leverage these concepts to devise a tractable comparison criterion (information sufficiency), leading to a task-agnostic and self-supervised ranking procedure. We demonstrate experimentally that our approach aligns closely with the capability of embedding models to facilitate various downstream tasks in both natural language processing and molecular biology. This effectively offers practitioners a valuable tool for prioritizing model trials.

2024-06-11

ArXiv (preprint)

Performance of generative pre-trained transformers (GPTs) in Certification Examination of the College of Family Physicians of Canada

Mehdi Mousavi

Shabnam Shafiee

Jason M Harley

Samira Abbasgholizadeh-Rahimi

Introduction The application of large language models such as generative pre-trained transformers (GPTs) has been promising in medical educa… (see more)tion, and its performance has been tested for different medical exams. This study aims to assess the performance of GPTs in responding to a set of sample questions of short-answer management problems (SAMPs) from the certification exam of the College of Family Physicians of Canada (CFPC). Method Between August 8th and 25th, 2023, we used GPT-3.5 and GPT-4 in five rounds to answer a sample of 77 SAMPs questions from the CFPC website. Two independent certified family physician reviewers scored AI-generated responses twice: first, according to the CFPC answer key (ie, CFPC score), and second, based on their knowledge and other references (ie, Reviews’ score). An ordinal logistic generalised estimating equations (GEE) model was applied to analyse repeated measures across the five rounds. Result According to the CFPC answer key, 607 (73.6%) lines of answers by GPT-3.5 and 691 (81%) by GPT-4 were deemed accurate. Reviewer’s scoring suggested that about 84% of the lines of answers provided by GPT-3.5 and 93% of GPT-4 were correct. The GEE analysis confirmed that over five rounds, the likelihood of achieving a higher CFPC Score Percentage for GPT-4 was 2.31 times more than GPT-3.5 (OR: 2.31; 95% CI: 1.53 to 3.47; p0.001). Similarly, the Reviewers’ Score percentage for responses provided by GPT-4 over 5 rounds were 2.23 times more likely to exceed th

2024-05-01

Family Medicine and Community Health (published)

Performance of generative pre-trained transformers (GPTs) in Certification Examination of the College of Family Physicians of Canada

Mehdi Mousavi

Shabnam Shafiee

Jason M Harley

Samira Abbasgholizadeh-Rahimi

2024-05-01

Family Medicine and Community Health (published)

Ensemble Distillation for Unsupervised Constituency Parsing

Behzad Shayegh

Yanshuai Cao

Xiaodan Zhu

Lili Mou

2024-01-16

ICLR.cc/2024/Conference (poster)

ECBD: Evidence-Centered Benchmark Design for NLP

Yu Lu Liu

Su Lin Blodgett

Jackie Chi

Kit Cheung

Q. Vera Liao

Alexandra Olteanu

Ziang Xiao

Benchmarking is seen as critical to assessing progress in NLP. However, creating a benchmark involves many design decisions (e.g., which dat… (see more)asets to include, which metrics to use) that often rely on tacit, untested assumptions about what the benchmark is intended to measure or is actually measuring. There is currently no principled way of analyzing these decisions and how they impact the validity of the benchmark's measurements. To address this gap, we draw on evidence-centered design in educational assessments and propose Evidence-Centered Benchmark Design (ECBD), a framework which formalizes the benchmark design process into five modules. ECBD specifies the role each module plays in helping practitioners collect evidence about capabilities of interest. Specifically, each module requires benchmark designers to describe, justify, and support benchmark design choices -- e.g., clearly specifying the capabilities the benchmark aims to measure or how evidence about those capabilities is collected from model responses. To demonstrate the use of ECBD, we conduct case studies with three benchmarks: BoolQ, SuperGLUE, and HELM. Our analysis reveals common trends in benchmark design and documentation that could threaten the validity of benchmarks' measurements.

2024-01-01

ACL (1) (published)

Balaur: Language Model Pretraining with Lexical Semantic Relations

Andrei Mircea

2023-12-01

Findings of the Association for Computational Linguistics: EMNLP 2023 (published)