Publications

A Robot Walks into a Bar: Can Language Models Serve as Creativity SupportTools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians
Piotr Mirowski
J Christopher Love
Juliette Love
Kory Mathewson
Shakir Mohamed
2024-06-04
The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Towards Geographic Inclusion in the Evaluation of Text-to-Image Models
Melissa Hall
Samuel J. Bell
Candace Ross
Adina Williams
Michal Drozdzal
Adriana Romero
Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of … (see more)thoroughly evaluating their performance and identifying potential biases. In pursuit of models that generate images that are realistic, diverse, visually appealing, and consistent with the given prompt, researchers and practitioners often turn to automated metrics to facilitate scalable and cost-effective performance profiling. However, commonly-used metrics often fail to account for the full diversity of human preference; often even in-depth human evaluations face challenges with subjectivity, especially as interpretations of evaluation criteria vary across regions and cultures. In this work, we conduct a large, cross-cultural study to study how much annotators in Africa, Europe, and Southeast Asia vary in their perception of geographic representation, visual appeal, and consistency in real and generated images from state-of-the art public APIs. We collect over 65,000 image annotations and 20 survey responses. We contrast human annotations with common automated metrics, finding that human preferences vary notably across geographic location and that current metrics do not fully account for this diversity. For example, annotators in different locations often disagree on whether exaggerated, stereotypical depictions of a region are considered geographically representative. In addition, the utility of automatic evaluations is dependent on assumptions about their set-up, such as the alignment of feature extractors with human perception of object similarity or the definition of"appeal"captured in reference datasets used to ground evaluations. We recommend steps for improved automatic and human evaluations.
2024-06-04
The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Visibility into AI Agents
Alan Chan
Carson Ezell
Max Kaufmann
Kevin Wei
Lewis Hammond
Herbie Bradley
Emma Bluemke
Nitarshan Rajkumar
David Krueger
Noam Kolt
Lennart Heim
Markus Anderljung
Increased delegation of commercial, scientific, governmental, and personal activities to AI agents -- systems capable of pursuing complex go… (see more)als with limited supervision -- may exacerbate existing societal risks and introduce new risks. Understanding and mitigating these risks involves critically evaluating existing governance structures, revising and adapting these structures where needed, and ensuring accountability of key stakeholders. Information about where, why, how, and by whom certain AI agents are used, which we refer to as visibility, is critical to these objectives. In this paper, we assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging. For each, we outline potential implementations that vary in intrusiveness and informativeness. We analyze how the measures apply across a spectrum of centralized through decentralized deployment contexts, accounting for various actors in the supply chain including hardware and software service providers. Finally, we discuss the implications of our measures for privacy and concentration of power. Further work into understanding the measures and mitigating their negative impacts can help to build a foundation for the governance of AI agents.
2024-06-04
The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Milnor-Myerson Games and The Principles of Artificial Principal-Agent Problems
Manfred Diaz
Joel Z Leibo
Liam Paull
In this paper, we introduce Milnor-Myerson games, a multiplayer interaction structure at the core of machine learning (ML), to shed light on… (see more) the fundamental principles and implications the artificial principal-agent problem has had in landmark ML results like AlphaGo and large language models (LLMs).
2024-06-03
rl-conference.cc/RLC/2024/Workshop/Finding_the_Frame (poster)
openreview.net
openreview.net
Sequential predictive learning is a unifying theory for hippocampal representation and replay
Daniel Levenstein
Aleksei Efremov
Roy Henha Eyono
Adrien Peyrache
Blake Richards
The mammalian hippocampus contains a cognitive map that represents an animal’s position in the environment 1 … (see more) and generates offline “replay” 2,3 for the purposes of recall 4 , planning 5,6 , and forming long term memories 7 . Recently, it’s been found that artificial neural networks trained to predict sensory inputs develop spatially tuned cells 8 , aligning with predictive theories of hippocampal function 9–11 . However, whether predictive learning can also account for the ability to produce offline replay is unknown. Here, we find that spatially-tuned cells, which robustly emerge from all forms of predictive learning, do not guarantee the presence of a cognitive map with the ability to generate replay. Offline simulations only emerged in networks that used recurrent connections and head-direction information to predict multi-step observation sequences, which promoted the formation of a continuous attractor reflecting the geometry of the environment. These offline trajectories were able to show wake-like statistics, autonomously replay recently experienced locations, and could be directed by a virtual head direction signal. Further, we found that networks trained to make cyclical predictions of future observation sequences were able to rapidly learn a cognitive map and produced sweeping representations of future positions reminiscent of hippocampal theta sweeps 12 . These results demonstrate how hippocampal-like representation and replay can emerge in neural networks engaged in predictive learning, and suggest that hippocampal theta sequences reflect a circuit that implements a data-efficient algorithm for sequential predictive learning. Together, this framework provides a unifying theory for hippocampal functions and hippocampal-inspired approaches to artificial intelligence.
2024-06-03
bioRxiv (preprint)
doi.org
From Feature Visualization to Visual Circuits: Effect of Adversarial Model Manipulation
Geraldin Nanfack
Michael Eickenberg
Eugene Belilovsky
Understanding the inner working functionality of large-scale deep neural networks is challenging yet crucial in several high-stakes applicat… (see more)ions. Mechanistic inter- pretability is an emergent field that tackles this challenge, often by identifying human-understandable subgraphs in deep neural networks known as circuits. In vision-pretrained models, these subgraphs are usually interpreted by visualizing their node features through a popular technique called feature visualization. Recent works have analyzed the stability of different feature visualization types under the adversarial model manipulation framework. This paper starts by addressing limitations in existing works by proposing a novel attack called ProxPulse that simultaneously manipulates the two types of feature visualizations. Surprisingly, when analyzing these attacks under the umbrella of visual circuits, we find that visual circuits show some robustness to ProxPulse. We, therefore, introduce a new attack based on ProxPulse that unveils the manipulability of visual circuits, shedding light on their lack of robustness. The effectiveness of these attacks is validated using pre-trained AlexNet and ResNet-50 models on ImageNet.
2024-06-02
ArXiv (preprint)
doi.org
arxiv.org
MOSEAC: Streamlined Variable Time Step Reinforcement Learning
Yong Wang
Giovanni Beltrame
2024-06-02
ArXiv (preprint)
doi.org
arxiv.org
Political Dynasties in Canada
Alex B. Rivard
Jean-François Godbout
Marc André Bodet
Using a unique dataset of legislators' electoral and biographical data in the Canadian provinces of Ontario, Quebec, New Brunswick, Nova Sco… (see more)tia and the federal parliament, this article analyses the extent to which family dynasties affected the career development of legislators since the mid-18th century. We find that the prevalence of dynasties was higher in provincial legislatures than it was in the federal parliament, that the number of dynasties in the Senate increased until the mid-20th century, and that the proportion of dynastic legislators at the subnational level was similar to the numbers seen in the United Kingdom during the early 19th century. Our results confirm the existence of a clear career benefit in terms of cabinet and senate appointments. In contrast to the American case and in line with the United Kingdom experience, we find no causal relationship between a legislator's tenure length and the presence of a dynasty.
2024-06-02
Government and Opposition (published)
doi.org
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages
Jiayi Wang
David Ifeoluwa Adelani
Sweta Agrawal
Marek Masiak
Ricardo Rei
Eleftheria Briakou
Marine Carpuat
Xuanli He
Sofia Bourhim
Andiswa Bukula
Muhidin A. Mohamed
Temitayo Olatoye
Tosin Adewumi
Hamam Mokayed
Christine Mwase
Wangui Kimotho
Foutse Yuehgoh
Aremu Anuoluwapo
Jessica Ojo
Shamsuddeen Hassan Muhammad … (see 41 more)
Salomey Osei
Abdul-Hakeem Omotayo
Chiamaka Ijeoma Chukwuneke
Perez Ogayo
Oumaima Hourrane
Salma El Anigri
Lolwethu Ndolela
Thabiso Mangwana
Shafie Abdi Mohamed
Hassan Ayinde
Ayinde Hassan
Oluwabusayo Olufunke Awoyomi
Lama Alkhaled
sana Sabah al-azzawi
Naome Etori
Millicent Ochieng
Clemencia Siro
Samuel Njoroge
Njoroge Kiragu
Eric Muchiri
Wangari Kimotho
Lyse Naomi Wamba
Daud Abolade
Simbiat Ajao
Iyanuoluwa Shode
Ricky Macharm
Ruqayya Nasir Iro
Saheed Salahudeen Abdullahi
Stephen Moore
Bernard Opoku
Zainab Akinjobi
Abeeb Afolabi
Nnaemeka Casmir Obiefuna
Onyekachi Ogbu
Sam Brian
Sam Ochieng’
Verrah Akinyi Otiende
CHINEDU EMMANUEL MBONU
Toadoum Sari Sakayo
Yao Lu
Pontus Stenetorp
Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measur… (see more)ing this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441).
2024-05-31
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (published)
doi.org
arxiv.org
Better entity matching with transformers through ensembles
Jwen Fai Low
Benjamin C. M. Fung
Pulei Xiong
2024-05-31
Knowledge-Based Systems (published)
doi.org
Immunotherapeutic targeting of surfaceome heterogeneity in AML
Marie-Eve Bordeleau
Éric Audemard
Arnaud Metois
Louis Theret
Véronique Lisi
Azer Farah
Jean-François Spinella
Jalila Chagraoui
Ossama Moujaber
Léo Aubert
Banafsheh Khakipoor
Laure Mallinger
Isabel Boivin
Nadine Mayotte
Azadeh Hajmirza
Éric Bonneil
Francois Beliveau
Sybille Pfammatter
Albert Feghaly
Geneviève Boucher … (see 9 more)
Patrick Gendron
Pierre Thibault
Frederic Barabe
Sébastien Lemieux
Guillaume Richard-Carpentier
Josée Hébert
Vincent-Philippe Lavallee
Philippe P. Roux
Guy Sauvageau
2024-05-31
Cell Reports (published)
doi.org
Learning Tabu Search Algorithms: A Scheduling Application
Nazgol Niroumandrad
Nadia Lahrichi
Andrea Lodi
. Metaheuristics are widely recognized as efficient approaches for many combinatorial problems. Studies to improve the performance of metahe… (see more)uristics have increasingly relied on the use of various methods either combining different metaheuristics or methods originating outside of the metaheuristic field. This paper presents a learning algorithm to improve tabu search by reducing its search space and the evaluation effort. We study the performance of a learning tabu search algorithm using classification methods in an attempt to select moves through the search space more wisely. The experimental results demonstrate the benefit of using a learning mechanism under deterministic and stochastic conditions.
2024-05-31
Computers & Operations Research (published)
doi.org