Publications

Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidance

Reyhane Askari Hemmat

Melissa Hall

Alicia Sun

Candace Ross

Michal Drozdzal

Adriana Romero Soriano

With the growing popularity of text-to-image generative models, there has been increasing focus on understanding their risks and biases. Rec… (see more)ent work has found that state-of-the-art models struggle to depict everyday objects with the true diversity of the real world and have notable gaps between geographic regions. In this work, we aim to increase the diversity of generated images of common objects such that per-region variations are representative of the real world. We introduce an inference time intervention, contextualized Vendi Score Guidance (c-VSG), that guides the backwards steps of latent diffusion models to increase the diversity of a sample as compared to a"memory bank"of previously generated images while constraining the amount of variation within that of an exemplar set of real-world contextualizing images. We evaluate c-VSG with two geographically representative datasets and find that it substantially increases the diversity of generated images, both for the worst performing regions and on average, while simultaneously maintaining or improving image quality and consistency. Additionally, qualitative analyses reveal that diversity of generated images is significantly improved, including along the lines of reductive region portrayals present in the original model. We hope that this work is a step towards text-to-image generative models that reflect the true geographic diversity of the world.

2024-06-06

ArXiv (preprint)

Black-Box Access is Insufficient for Rigorous AI Audits

Stephen Casper

Carson Ezell

Charlotte Siegmann

Noam Kolt

Taylor Lynn Curtis

Benjamin Bucknall

Andreas Haupt

Kevin Wei

Jérémy Scheurer

Marius Hobbhahn

Lee Sharkey

Satyapriya Krishna

Marvin Von Hagen

Silas Alberti

Alan Chan

Qinyi Sun

Michael Gerovitch

David Bau

Max Tegmark

David Scott Krueger … (see 1 more)

Dylan Hadfield-Menell

External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depe… (see more)nds on the degree of system access granted to auditors. Recent audits of state-of-the-art AI systems have primarily relied on black-box access, in which auditors can only query the system and observe its outputs. However, white-box access to the system's inner workings (e.g., weights, activations, gradients) allows an auditor to perform stronger attacks, more thoroughly interpret models, and conduct fine-tuning. Meanwhile, outside-the-box access to its training and deployment information (e.g., methodology, code, documentation, hyperparameters, data, deployment details, findings from internal evaluations) allows for auditors to scrutinize the development process and design more targeted evaluations. In this paper, we examine the limitations of black-box audits and the advantages of white- and outside-the-box audits. We also discuss technical, physical, and legal safeguards for performing these audits with minimal security risks. Given that different forms of access can lead to very different levels of evaluation, we conclude that (1) transparency regarding the access and methods used by auditors is necessary to properly interpret audit results, and (2) white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.

2024-06-05

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

Characterizing and Classifying Developer Forum Posts with their Intentions

Xingfang Wu

Eric Laufer

Heng Li

Foutse Khomh

Santhosh Srinivasan

Jayden Luo

With the rapid growth of the developer community, the amount of posts on online technical forums has been growing rapidly, which poses diffi… (see more)culties for users to filter useful posts and find important information. Tags provide a concise feature dimension for users to locate their interested posts and for search engines to index the most relevant posts according to the queries. However, most tags are only focused on the technical perspective (e.g., program language, platform, tool). In most cases, forum posts in online developer communities reveal the author's intentions to solve a problem, ask for advice, share information, etc. The modeling of the intentions of posts can provide an extra dimension to the current tag taxonomy. By referencing previous studies and learning from industrial perspectives, we create a refined taxonomy for the intentions of technical forum posts. Through manual labeling and analysis on a sampled post dataset extracted from online forums, we understand the relevance between the constitution of posts (code, error messages) and their intentions. Furthermore, inspired by our manual study, we design a pre-trained transformer-based model to automatically predict post intentions. The best variant of our intention prediction framework, which achieves a Micro F1-score of 0.589, Top 1-3 accuracy of 62.6% to 87.8%, and an average AUC of 0.787, outperforms the state-of-the-art baseline approach. Our characterization and automated classification of forum posts regarding their intentions may help forum maintainers or third-party tool developers improve the organization and retrieval of posts on technical forums. We have released our annotated dataset and codes in our supplementary material package.

2024-06-05

Empirical Software Engineering (published)

Individual Brain Charting dataset extension, third release for movie watching and retinotopy data

Ana Lúısa Pinho

Hugo Richard

Ana Fernanda Ponce

Michael Eickenberg

Alexis Amadon

Elvis Dohmatob

Isabelle Denghien

Juan Jesús Torre

Swetha Shankar

Himanshu Aggarwal

Alexis Thual

Thomas Chapalain

Chantal Ginisty

Séverine Becuwe-Desmidt

Séverine Roger

Yann Lecomte

Valérie Berland

Laurence Laurier

Véronique Joly-Testault

Gaëlle Médiouni-Cloarec … (see 6 more)

Christine Doublé

Bernadette Martins

Gael Varoquaux

Stanislas Dehaene

Lucie Hertz-Pannier

Bertrand Thirion

2024-06-05

Scientific Data (published)

IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models

Jessica Ojo

Israel Abebe Azime

Zhuang Yun Jian

Jesujoba Oluwadara Alabi

Xuanli He

Millicent Ochieng

Sara Hooker

Andiswa Bukula

En-Shiun Annie Lee

Chiamaka Ijeoma Chukwuneke

Happy Buzaaba

Blessing Kudzaishe Sibanda

Godson Kalipe

Jonathan Mukiibi

Salomon Kabongo

Foutse Yuehgoh

M. Setaka

Lolwethu Ndolela

Nkiruka Bridget Odu … (see 6 more)

Rooweither Mabuya

Shamsuddeen Hassan Muhammad

Salomey Osei

Sokhar Samb

Tadesse Kebede Guge

Pontus Stenetorp

Despite the widespread adoption of Large language models (LLMs), their remarkable capabilities remain limited to a few high-resource languag… (see more)es. Additionally, many low-resource languages (e.g. African languages) are often evaluated only on basic text classification tasks due to the lack of appropriate or comprehensive benchmarks outside of high-resource languages. In this paper, we introduce IrokoBench -- a human-translated benchmark dataset for 16 typologically-diverse low-resource African languages covering three tasks: natural language inference~(AfriXNLI), mathematical reasoning~(AfriMGSM), and multi-choice knowledge-based QA~(AfriMMLU). We use IrokoBench to evaluate zero-shot, few-shot, and translate-test settings~(where test sets are translated into English) across 10 open and four proprietary LLMs. Our evaluation reveals a significant performance gap between high-resource languages~(such as English and French) and low-resource African languages. We observe a significant performance gap between open and proprietary models, with the highest performing open model, Aya-101 only at 58\% of the best-performing proprietary model GPT-4o performance. Machine translating the test set to English before evaluation helped to close the gap for larger models that are English-centric, like LLaMa 3 70B. These findings suggest that more efforts are needed to develop and adapt LLMs for African languages.

2024-06-05

ArXiv (preprint)

IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models

Jessica Ojo

Israel Abebe Azime

Zhuang Yun Jian

Jesujoba Oluwadara Alabi

Xuanli He

Millicent Ochieng

Sara Hooker

Andiswa Bukula

En-Shiun Annie Lee

Chiamaka Ijeoma Chukwuneke

Happy Buzaaba

Blessing Kudzaishe Sibanda

Godson Kalipe

Jonathan Mukiibi

Salomon Kabongo

Foutse Yuehgoh

M. Setaka

Lolwethu Ndolela

Nkiruka Bridget Odu … (see 6 more)

Rooweither Mabuya

Shamsuddeen Hassan Muhammad

Salomey Osei

Sokhar Samb

Tadesse Kebede Guge

Pontus Stenetorp

Despite the widespread adoption of Large language models (LLMs), their remarkable capabilities remain limited to a few high-resource languag… (see more)es. Additionally, many low-resource languages (\eg African languages) are often evaluated only on basic text classification tasks due to the lack of appropriate or comprehensive benchmarks outside of high-resource languages. In this paper, we introduce IrokoBench -- a human-translated benchmark dataset for 17 typologically-diverse low-resource African languages covering three tasks: natural language inference~(AfriXNLI), mathematical reasoning~(AfriMGSM), and multi-choice knowledge-based question answering~(AfriMMLU). We use IrokoBench to evaluate zero-shot, few-shot, and translate-test settings~(where test sets are translated into English) across 10 open and six proprietary LLMs. Our evaluation reveals a significant performance gap between high-resource languages~(such as English and French) and low-resource African languages. We observe a significant performance gap between open and proprietary models, with the highest performing open model, Gemma 2 27B only at 63\% of the best-performing proprietary model GPT-4o performance. In addition, machine translating the test set to English before evaluation helped to close the gap for larger models that are English-centric, such as Gemma 2 27B and LLaMa 3.1 70B. These findings suggest that more efforts are needed to develop and adapt LLMs for African languages.

2024-06-05

ArXiv (preprint)

Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework

Eshta Bhardwaj

Harshit Gujral

Siyi Wu

Ciara Zogheib

Tegan Maharaj

Christoph Becker

Studies of dataset development in machine learning call for greater attention to the data practices that make model development possible and… (see more) shape its outcomes. Many argue that the adoption of theory and practices from archives and data curation fields can support greater fairness, accountability, transparency, and more ethical machine learning. In response, this paper examines data practices in machine learning dataset development through the lens of data curation. We evaluate data practices in machine learning as data curation practices. To do so, we develop a framework for evaluating machine learning datasets using data curation concepts and principles through a rubric. Through a mixed-methods analysis of evaluation results for 25 ML datasets, we study the feasibility of data curation principles to be adopted for machine learning data work in practice and explore how data curation is currently performed. We find that researchers in machine learning, which often emphasizes model development, struggle to apply standard data curation principles. Our findings illustrate difficulties at the intersection of these fields, such as evaluating dimensions that have shared terms in both fields but non-shared meanings, a high degree of interpretative flexibility in adapting concepts without prescriptive restrictions, obstacles in limiting the depth of data curation expertise needed to apply the rubric, and challenges in scoping the extent of documentation dataset creators are responsible for. We propose ways to address these challenges and develop an overall framework for evaluation that outlines how data curation concepts and methods can inform machine learning data practices.

2024-06-05

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

Meta's AI translation model embraces overlooked languages.

2024-06-05

Nature (published)

Meta's AI translation model embraces overlooked languages.

2024-06-05

Nature (published)

Noisy Data Visualization using Functional Data Analysis

Haozhe Chen

Andres Felipe Duque Correa

Guy Wolf

Kevin R. Moon

Data visualization via dimensionality reduction is an important tool in exploratory data analysis. However, when the data are noisy, many ex… (see more)isting methods fail to capture the underlying structure of the data. The method called Empirical Intrinsic Geometry (EIG) was previously proposed for performing dimensionality reduction on high dimensional dynamical processes while theoretically eliminating all noise. However, implementing EIG in practice requires the construction of high-dimensional histograms, which suffer from the curse of dimensionality. Here we propose a new data visualization method called Functional Information Geometry (FIG) for dynamical processes that adapts the EIG framework while using approaches from functional data analysis to mitigate the curse of dimensionality. We experimentally demonstrate that the resulting method outperforms a variant of EIG designed for visualization in terms of capturing the true structure, hyperparameter robustness, and computational speed. We then use our method to visualize EEG brain measurements of sleep activity.

2024-06-05

ArXiv (preprint)

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

Juliette Love

Kory Wallace Mathewson

Shakir Mohamed

2024-06-05

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