Publications

Towards Geographic Inclusion in the Evaluation of Text-to-Image Models

Melissa Hall

Samuel J. Bell

Candace Ross

Adina Williams

Michal Drozdzal

Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of … (voir plus)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.

2024-06-05

The 2024 ACM Conference on Fairness, Accountability, and Transparency (publié)

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 Scott Krueger

Noam Kolt

Lennart Heim

Markus Anderljung

2024-06-05

The 2024 ACM Conference on Fairness, Accountability, and Transparency (publié)

doi.org

arxiv.org

Efficient Leverage Score Sampling for Tensor Train Decomposition

Vivek Bharadwaj

Beheshteh T. Rakhshan

Osman Asif Malik

Guillaume Rabusseau

2024-06-04

ArXiv (prépublication)

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… (voir plus) the fundamental principles and implications the artificial principal-agent problem has had in landmark ML results like AlphaGo and large language models (LLMs).

2024-06-04

rl-conference.cc/RLC/2024/Workshop/Finding_the_Frame (poster)

openreview.net

PETRA: Parallel End-to-end Training with Reversible Architectures

Stephane Rivaud

Louis Fournier

Thomas Pumir

Eugene Belilovsky

Michael Eickenberg

Edouard Oyallon

Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep l… (voir plus)earning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective model parallelism by enabling stages (i.e., a set of layers) to compute independently on different devices, while only needing to communicate activations and gradients between each other. By decoupling the forward and backward passes and keeping a single updated version of the parameters, the need for weight stashing is also removed. We develop a custom autograd-like training framework for PETRA, and we demonstrate its effectiveness on CIFAR-10, ImageNet32, and ImageNet, achieving competitive accuracies comparable to backpropagation using ResNet-18, ResNet-34, and ResNet-50 models.

2024-06-04

ArXiv (prépublication)

doi.org

openreview.net

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning

Khimya Khetarpal

Zhaohan Daniel Guo

Bernardo Avila Pires

Yunhao Tang

Clare Lyle

Mark Rowland

Nicolas Heess

Diana Borsa

Arthur Guez

Will Dabney

2024-06-04

ArXiv (prépublication)

doi.org

arxiv.org

ACCO: Accumulate while you Communicate, Hiding Communications in Distributed LLM Training

Adel Nabli

Louis Fournier

Pierre Erbacher

Louis Serrano

Eugene Belilovsky

Edouard Oyallon

2024-06-03

ArXiv (prépublication)

doi.org

arxiv.org

From Feature Visualization to Visual Circuits: Effect of Adversarial Model Manipulation

G'eraldin 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… (voir plus)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.

2024-06-03

ArXiv (prépublication)

doi.org

arxiv.org

MOSEAC: Streamlined Variable Time Step Reinforcement Learning

Dong Wang

Giovanni Beltrame

2024-06-03

ArXiv (prépublication)

doi.org

arxiv.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 … (voir 41 de plus)

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… (voir plus)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).

2024-06-01

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (publié)

doi.org

arxiv.org

Better entity matching with transformers through ensembles

Jwen Fai Low

Benjamin Fung

Pulei Xiong

2024-06-01

Knowledge-Based Systems (publié)

doi.org

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

Philipp Thölke

Maxine Arcand-Lavigne