Portrait of Ines Arous

Ines Arous

ines.arous@mila.quebec
Independent visiting researcher - McGill University
Supervisor
Jackie Cheung
Research Topics
Natural Language Processing
Probabilistic Models
Website
Google Scholar
GitHub

Publications

Hands-Off or Hands-On? Variation in Area Chair Practices and Implications for AI Support
Ines Arous
Neha Nayak Kennard
Andrei Mircea
Emily Kuang
Jackie Chi Kit Cheung
Andrew McCallum
Area chairs (ACs) play a critical role in the peer-review process, managing conflicts and ensuring fair outcomes. Although AI tools have bee… (see more)n proposed to support ACs, little is known about the challenges they face and their perceptions of these technologies. In this paper, we conduct interviews including a design probe with 27 ACs in AI to explore their challenges, strategies, and perspectives on potential AI tools. Through thematic analysis, we identify key tensions arising from the growing volume of submissions, uneven reviewer expertise, and the complex task of managing the relationship between reviewers and authors. Most importantly, we find substantial variation in how ACs engage with submissions and influence outcomes: some adopt a largely hands-off approach, while others take a more hands-on role in guiding discussions and decisions. This variation challenges the notion of a single, universal AC practice and highlights the need to account for diverse approaches. When reflecting on the potential use of AI tools, ACs expressed a cautious stance, drawing on their domain knowledge and heightened awareness of AI limitations. From these findings, we derive three design implications: tailoring AI assistance to diverse AC practices, design assistance for discussion moderation, and embedding human-centered AI principles that preserve human agency in decision-making.
2026-08-06
arXiv (preprint)
doi.org
arxiv.org
Active Learning with Non-Uniform Costs for African Natural Language Processing
Bonaventure F. P. Dossou
Ines Arous
Audrey Durand
Jackie Chi Kit Cheung
2025-12-31
Conference of the European Chapter of the Association for Computational Linguistics (published)
doi.org
Partial Perspectives: How LLMs Handle Logically Inconsistent Knowledge in Reasoning Tasks
Zichao Li
Ines Arous
Jackie CK Cheung
Most natural language reasoning tasks in the research community assume consistent input knowledge. Nevertheless, real-world scenarios often … (see more)involve inconsistent information, which might lead to divergent conclusions and are typically associated with varying levels of uncertainty. This raises a key research question: can large language models (LLMs) effectively handle uncertainty in their reasoning process to maximize knowledge consistency? In this paper, we propose a framework for evaluating reasoning over inconsistent knowledge. Our approach models uncertainty via weights of logical rules, leveraging Markov logic networks (MLN), which integrate probabilistic reasoning with first-order logic. This enables us to quantify inconsistencies in knowledge bases, and hence rigorously evaluate LLM reasoning. We introduce two tasks using this framework: 1) QA, which involves answering questions by integrating inconsistent knowledge; and 2) knowledge rectification, where we aim to rectify language models' acquired knowledge to improve consistency. We curate a dataset of 3,000 MLN-formatted knowledge bases to implement these tasks. We evaluate state-of-the-art LLMs on these tasks and highlight their limitations in uncertainty-aware reasoning over inconsistent logical knowledge.
2025-07-06
colmweb.org/COLM/2025/Conference (accepted)
openreview.net
openreview.net
Rethinking Full Finetuning from Pretraining Checkpoints in Active Learning for African Languages
Bonaventure F. P. Dossou
Ines Arous
Jackie CK Cheung
2025-06-21
aclweb.org/ACL/2025/SRW (poster)
openreview.net
openreview.net
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews
Maxime Darrin
Ines Arous
Pablo Piantanida
Jackie CK Cheung
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.
2024-06-10
ArXiv (preprint)
doi.org
arxiv.org
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness
Zichao Li
Ines Arous
Siva Reddy
Jackie C.K. Cheung
The potential of using a large language model (LLM) as a knowledge base (KB) has sparked significant interest. To maintain the knowledge acq… (see more)uired by LLMs, we need to ensure that the editing of learned facts respects internal logical constraints, which are known as dependency of knowledge. Existing work on editing LLMs has partially addressed the issue of dependency, when the editing of a fact should apply to its lexical variations without disrupting irrelevant ones. However, they neglect the dependency between a fact and its logical implications. We propose an evaluation protocol with an accompanying question-answering dataset, StandUp, that provides a comprehensive assessment of the editing process considering the above notions of dependency. Our protocol involves setting up a controlled environment in which we edit facts and monitor their impact on LLMs, along with their implications based on If-Then rules. Extensive experiments on StandUp show that existing knowledge editing methods are sensitive to the surface form of knowledge, and that they have limited performance in inferring the implications of edited facts.
2023-10-06
Conference on Empirical Methods in Natural Language Processing (accepted)
doi.org
openreview.net