Publications

Gaps Between Research and Practice When Measuring Representational Harms Caused by LLM-Based Systems

Emma Harvey

Emily Sheng

Su Lin Blodgett

Alexandra Chouldechova

Jean Garcia-Gathright

Hanna Wallach

To facilitate the measurement of representational harms caused by large language model (LLM)-based systems, the NLP research community has p… (voir plus)roduced and made publicly available numerous measurement instruments, including tools, datasets, metrics, benchmarks, annotation instructions, and other techniques. However, the research community lacks clarity about whether and to what extent these instruments meet the needs of practitioners tasked with developing and deploying LLM-based systems in the real world, and how these instruments could be improved. Via a series of semi-structured interviews with practitioners in a variety of roles in different organizations, we identify four types of challenges that prevent practitioners from effectively using publicly available instruments for measuring representational harms caused by LLM-based systems: (1) challenges related to using publicly available measurement instruments; (2) challenges related to doing measurement in practice; (3) challenges arising from measurement tasks involving LLM-based systems; and (4) challenges specific to measuring representational harms. Our goal is to advance the development of instruments for measuring representational harms that are well-suited to practitioner needs, thus better facilitating the responsible development and deployment of LLM-based systems.

2024-11-23

ArXiv (prépublication)

Gaps Between Research and Practice When Measuring Representational Harms Caused by LLM-Based Systems

Emma Harvey

Emily Sheng

Su Lin Blodgett

Alexandra Chouldechova

Jean Garcia-Gathright

Hanna Wallach

To facilitate the measurement of representational harms caused by large language model (LLM)-based systems, the NLP research community has p… (voir plus)roduced and made publicly available numerous measurement instruments, including tools, datasets, metrics, benchmarks, annotation instructions, and other techniques. However, the research community lacks clarity about whether and to what extent these instruments meet the needs of practitioners tasked with developing and deploying LLM-based systems in the real world, and how these instruments could be improved. Via a series of semi-structured interviews with practitioners in a variety of roles in different organizations, we identify four types of challenges that prevent practitioners from effectively using publicly available instruments for measuring representational harms caused by LLM-based systems: (1) challenges related to using publicly available measurement instruments; (2) challenges related to doing measurement in practice; (3) challenges arising from measurement tasks involving LLM-based systems; and (4) challenges specific to measuring representational harms. Our goal is to advance the development of instruments for measuring representational harms that are well-suited to practitioner needs, thus better facilitating the responsible development and deployment of LLM-based systems.

2024-11-23

ArXiv (prépublication)

Laurence Perreault-Levasseur

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Connor Stone

Alexandre Adam

Adam Coogan

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Ronan Legin

Charles Wilson

Gabriel Missael Barco

Yashar Hezaveh

2024-11-22

Journal of Open Source Software (publié)

"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models

Angel Hsing-Chi Hwang

Q. V. Liao

Su Lin Blodgett

Adam Trischler

2024-11-20

ArXiv (prépublication)

"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models

Angel Hsing-Chi Hwang

Q. V. Liao

Su Lin Blodgett

Adam Trischler

Given the rising proliferation and diversity of AI writing assistance tools, especially those powered by large language models (LLMs), both … (voir plus)writers and readers may have concerns about the impact of these tools on the authenticity of writing work. We examine whether and how writers want to preserve their authentic voice when co-writing with AI tools and whether personalization of AI writing support could help achieve this goal. We conducted semi-structured interviews with 19 professional writers, during which they co-wrote with both personalized and non-personalized AI writing-support tools. We supplemented writers' perspectives with opinions from 30 avid readers about the written work co-produced with AI collected through an online survey. Our findings illuminate conceptions of authenticity in human-AI co-creation, which focus more on the process and experience of constructing creators' authentic selves. While writers reacted positively to personalized AI writing tools, they believed the form of personalization needs to target writers' growth and go beyond the phase of text production. Overall, readers' responses showed less concern about human-AI co-writing. Readers could not distinguish AI-assisted work, personalized or not, from writers' solo-written work and showed positive attitudes toward writers experimenting with new technology for creative writing.

2024-11-20

ArXiv (prépublication)

Effectiveness of primary repair for low anorectal malformations in Uganda.

Felix Oyania

Sarah Ullrich

Zane Hellmann

Caroline Q. Stephens

Meera Kotagal

Sarah Jane Commander

Amy M. Shui

Martin Situma

Charles Newton Odongo

Olivia Kituuka

Francis Bajunirwe

Doruk Ozgediz

Dan Poenaru

2024-11-19

Pediatric surgery international (Print) (publié)

Effectiveness of primary repair for low anorectal malformations in Uganda.

Felix Oyania

Sarah Ullrich

Zane J. Hellmann

Caroline Q. Stephens

Meera Kotagal

Sarah Jane Commander

Amy M. Shui

Martin Situma

Charles Newton Odongo

Olivia Kituuka

Francis Bajunirwe

Doruk Ozgediz

Dan Poenaru

2024-11-19

Pediatric surgery international (Print) (publié)

Effectiveness of primary repair for low anorectal malformations in Uganda.

Felix Oyania

Sarah Ullrich

Zane Hellmann

Caroline Q. Stephens

Meera Kotagal

Sarah Jane Commander

Amy M. Shui

Martin Situma

Charles Newton Odongo

Olivia Kituuka

Francis Bajunirwe

Doruk Ozgediz

Dan Poenaru

2024-11-19

Pediatric Surgery International (publié)

Sketch-guided Cage-based 3D Gaussian Splatting Deformation

Tianhao Xie

Noam Aigerman

Eugene Belilovsky

Tiberiu Popa

3D Gaussian Splatting (GS) is one of the most promising novel 3D representations that has received great interest in computer graphics and c… (voir plus)omputer vision. While various systems have introduced editing capabilities for 3D GS, such as those guided by text prompts, fine-grained control over deformation remains an open challenge. In this work, we present a novel sketch-guided 3D GS deformation system that allows users to intuitively modify the geometry of a 3D GS model by drawing a silhouette sketch from a single viewpoint. Our approach introduces a new deformation method that combines cage-based deformations with a variant of Neural Jacobian Fields, enabling precise, fine-grained control. Additionally, it leverages large-scale 2D diffusion priors and ControlNet to ensure the generated deformations are semantically plausible. Through a series of experiments, we demonstrate the effectiveness of our method and showcase its ability to animate static 3D GS models as one of its key applications.

2024-11-19

ArXiv (prépublication)

Towards Understanding the Impact of Data Bugs on Deep Learning Models in Software Engineering

Mehil B. Shah

Mohammad Masudur Rahman

Foutse Khomh

Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). Ho… (voir plus)wever, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.

2024-11-19

ArXiv (prépublication)

Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML

Prakhar Ganeesh

Usman Gohar

Lu Cheng

Golnoosh Farnadi

With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often … (voir plus)compared against each other to find the best method. These benchmarking efforts tend to use a common setup for evaluation under the assumption that providing a uniform environment ensures a fair comparison. However, bias mitigation techniques are sensitive to hyperparameter choices, random seeds, feature selection, etc., meaning that comparison on just one setting can unfairly favour certain algorithms. In this work, we show significant variance in fairness achieved by several algorithms and the influence of the learning pipeline on fairness scores. We highlight that most bias mitigation techniques can achieve comparable performance, given the freedom to perform hyperparameter optimization, suggesting that the choice of the evaluation parameters-rather than the mitigation technique itself-can sometimes create the perceived superiority of one method over another. We hope our work encourages future research on how various choices in the lifecycle of developing an algorithm impact fairness, and trends that guide the selection of appropriate algorithms.

2024-11-17

ArXiv (prépublication)