Portrait de Anne Imouza n'est pas disponible

Anne Imouza

anne.imouza@mila.quebec
Doctorat - McGill
Superviseur⋅e principal⋅e
Jean-François Godbout
Co-supervisor
Reihaneh Rabbany
Sujets de recherche
Modèles probabilistes
LinkedIn

Publications

StabilityBench: Benchmarking Instability in LLMs
Emma Kondrup
Zachary Yang
Anne Imouza
Reihaneh Rabbany
AI Assistants are increasingly deployed in high-stakes settings, such as healthcare or government services. Yet their real-world behavior re… (voir plus)mains poorly understood due to strong context dependence. Current evaluation protocols follow a defense-in-depth paradigm with compounding layers of safeguards, ranging from traditional benchmarks to live or adversarial testing. Such benchmarks remain largely static and single-turn, limiting their ability to capture real-world variability in conversational settings. We propose StabilityBench, a principled, general and model-agnostic benchmark operator that transforms single-turn benchmark queries into multi-turn interaction histories. StabilityBench augments existing benchmarks by injecting realistic user simulations, through demographic proxies or sycophantic baits, while preserving original task intent. We apply StabilityBench to four benchmarks spanning mathematical reasoning, health question-answering and safety, and evaluate nine large language models under these conditions. Our results show that model performance is consistently unstable under these injections, with considerable performance degradations on three out of four benchmarks studied. These highlight important limitations of static evaluations and motivate more realistic evaluation settings. To this end, we propose StabilityBench-Mini: a size-preserving variant of StabilityBench that samples across diversification axes, enabling more realistic evaluation without increasing costs.
2026-07-16
arXiv (prépublication)
doi.org
arxiv.org
Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada
Zachary Yang
Anne Imouza
Maximilian Puelma Touzel
C'ecile Amadoro
Gabrielle Desrosiers-Brisebois
Kellin Pelrine
Sacha Lévy
Jean-François Godbout
Reihaneh Rabbany
Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded … (voir plus)specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.
2024-07-02
ArXiv (prépublication)
doi.org
arxiv.org
Party Prediction for Twitter
Kellin Pelrine
Anne Imouza
Zachary Yang
Jacob-Junqi Tian
Sacha Lévy
Gabrielle Desrosiers-Brisebois
Aarash Feizi
Cécile Amadoro
Andre Blais
Jean-François Godbout
Reihaneh Rabbany
A large number of studies on social media compare the behaviour of users from different political parties. As a basic step, they employ a pr… (voir plus)edictive model for inferring their political affiliation. The accuracy of this model can change the conclusions of a downstream analysis significantly, yet the choice between different models seems to be made arbitrarily. In this paper, we provide a comprehensive survey and an empirical comparison of the current party prediction practices and propose several new approaches which are competitive with or outperform state-of-the-art methods, yet require less computational resources. Party prediction models rely on the content generated by the users (e.g., tweet texts), the relations they have (e.g., who they follow), or their activities and interactions (e.g., which tweets they like). We examine all of these and compare their signal strength for the party prediction task. This paper lets the practitioner select from a wide range of data types that all give strong performance. Finally, we conduct extensive experiments on different aspects of these methods, such as data collection speed and transfer capabilities, which can provide further insights for both applied and methodological research.
2024-05-27
Proceedings of the International AAAI Conference on Web and Social Media (publié)
doi.org
arxiv.org
Uncertainty Resolution in Misinformation Detection
Yury Orlovskiy
Camille Thibault
Anne Imouza
Jean-François Godbout
Reihaneh Rabbany
Kellin Pelrine
2024-01-01
arXiv (prépublication)
doi.org
arxiv.org
Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
Kellin Pelrine
Anne Imouza
Meilina Reksoprodjo
Camille Thibault
Caleb Gupta
Joel Christoph
Reihaneh Rabbany
Jean-François Godbout
Published online: 24 May 2023
2023-11-30
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
openreview.net
Online Partisan Polarization of COVID-19
Zachary Yang
Anne Imouza
Kellin Pelrine
Sacha Lévy
Jiewen Liu
Gabrielle Desrosiers-Brisebois
Jean-François Godbout
Andre Blais
Reihaneh Rabbany
In today’s age of (mis)information, many people utilize various social media platforms in an attempt to shape public opinion on sever… (voir plus)al important issues, including elections and the COVID-19 pandemic. These two topics have recently become intertwined given the importance of complying with public health measures related to COVID-19 and politicians’ management of the pandemic. Motivated by this, we study the partisan polarization of COVID-19 discussions on social media. We propose and utilize a novel measure of partisan polarization to analyze more than 380 million posts from Twitter and Parler around the 2020 US presidential election. We find strong correlation between peaks in polarization and polarizing events, such as the January 6th Capitol Hill riot. We further classify each post into key COVID-19 issues of lockdown, masks, vaccines, as well as miscellaneous, to investigate both the volume and polarization on these topics and how they vary through time. Parler includes more negative discussions around lockdown and masks, as expected, but not much around vaccines. We also observe more balanced discussions on Twitter and a general disconnect between the discussions on Parler and Twitter.
2021-11-30
2021 International Conference on Data Mining Workshops (ICDMW) (inconnu)
doi.org