Author name cluster

Margaret Mitchell

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor

Alexandra Olteanu
Su Lin Blodgett
Agathe Balayn
Angelina Wang
Fernando Diaz
Flavio Calmon
Margaret Mitchell
Michael Ekstrand

In AI research and practice, rigor remains largely understood in terms of methodological rigor---such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including overblown claims about the capabilities of AI systems. Our position is that a broader conception of what rigorous AI research and practice should entail is needed. We believe such a conception---in addition to a more expansive understanding of 1) methodological rigor---should include aspects related to 2) what background knowledge informs what to work on (epistemic rigor); 3) how disciplinary, community, or personal norms, standards, or beliefs influence the work (normative rigor); 4) how clearly articulated the theoretical constructs under use are (conceptual rigor); 5) what is reported and how (reporting rigor); and 6) how well-supported the inferences from existing evidence are (interpretative rigor). In doing so, we also provide useful language and a framework for much needed dialogue about the AI community's work by researchers, policymakers, journalists, and other stakeholders.

PDF Details

NeurIPS Conference 2024 Conference Paper

The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Guilherme Penedo
Hynek Kydlíček
Loubna B. allal
Anton Lozhkov
Margaret Mitchell
Colin Raffel
Leandro Von Werra
Thomas Wolf

The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs like Llama 3 and Mixtral are not publicly available and very little is known about how they were created. In this work, we introduce FineWeb, a 15-trillion token dataset derived from 96 Common Crawl snapshots that produces better-performing LLMs than other open pretraining datasets. To advance the understanding of how best to curate high-quality pretraining datasets, we carefully document and ablate all of the design choices used in FineWeb, including in-depth investigations of deduplication and filtering strategies. In addition, we introduce FineWeb-Edu, a 1. 3-trillion token collection of educational text filtered from FineWeb. LLMs pretrained on FineWeb-Edu exhibit dramatically better performance on knowledge- and reasoning-intensive benchmarks like MMLU and ARC. Along with our datasets, we publicly release our data curation codebase and all of the models trained during our ablation experiments.

PDF Details DOI

NeurIPS Conference 2023 Conference Paper

Stable Bias: Evaluating Societal Representations in Diffusion Models

Sasha Luccioni
Christopher Akiki
Margaret Mitchell
Yacine Jernite

As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingly prevalent and seeing growing adoption as commercial services, characterizing the social biases they exhibit is a necessary first step to lowering their risk of discriminatory outcomes. This evaluation, however, is made more difficult by the synthetic nature of these systems’ outputs: common definitions of diversity are grounded in social categories of people living in the world, whereas the artificial depictions of fictive humans created by these systems have no inherent gender or ethnicity. To address this need, we propose a new method for exploring the social biases in TTI systems. Our approach relies on characterizing the variation in generated images triggered by enumerating gender and ethnicity markers in the prompts, and comparing it to the variation engendered by spanning different professions. This allows us to (1) identify specific bias trends, (2) provide targeted scores to directly compare models in terms of diversity and representation, and (3) jointly model interdependent social variables to support a multidimensional analysis. We leverage this method to analyze images generated by 3 popular TTI systems (Dall·E 2, Stable Diffusion v 1. 4 and 2) and find that while all of their outputs show correlations with US labor demographics, they also consistently under-represent marginalized identities to different extents. We also release the datasets and low-code interactive bias exploration platforms developed forthis work, as well as the necessary tools to similarly evaluate additional TTI systems.

PDF Details

TMLR Journal 2023 Journal Article

The Stack: 3 TB of permissively licensed source code

Denis Kocetkov
Raymond Li
Loubna Ben allal
Jia Li
Chenghao Mou
Yacine Jernite
Margaret Mitchell
Carlos Muñoz Ferrandis

Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)--not only for natural language processing but also for code understanding and generation. To stimulate open and responsible research on LLMs for code, we introduce The Stack, a 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. We describe how we collect the full dataset, construct a permissively licensed subset, present a data governance plan, discuss limitations, and show promising results on text2code benchmarks by training 350M-parameter decoders on different Python subsets. We find that (1) near-deduplicating the data significantly boosts performance across all experiments, and (2) it is possible to match previously reported HumanEval and MBPP performance using only permissively licensed data. We make the dataset available at https://hf.co/BigCode, provide a tool called "Am I in The Stack" for developers to search The Stack for copies of their code (https://hf.co/spaces/bigcode/in-the-stack), and provide a process for code to be removed from the dataset.

PDF Details

NeurIPS Conference 2022 Conference Paper

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

Hugo Laurençon
Lucile Saulnier
Thomas Wang
Christopher Akiki
Albert Villanova del Moral
Teven Le Scao
Leandro Von Werra
Chenghao Mou

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1. 6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus.

PDF Details

AAAI Conference 2016 Conference Paper

Microsummarization of Online Reviews: An Experimental Study

Rebecca Mason
Benjamin Gaska
Benjamin Van Durme
Pallavi Choudhury
Ted Hart
Bill Dolan
Kristina Toutanova
Margaret Mitchell

Mobile and location-based social media applications provide platforms for users to share brief opinions about products, venues, and services. These quickly typed opinions, or microreviews, are a valuable source of current sentiment on a wide variety of subjects. However, there is currently little research on how to mine this information to present it back to users in easily consumable way. In this paper, we introduce the task of microsummarization, which combines sentiment analysis, summarization, and entity recognition in order to surface key content to users. We explore unsupervised and supervised methods for this task, and ﬁnd we can reliably extract relevant entities and the sentiment targeted towards them using crowdsourced labels as supervision. In an end-to-end evaluation, we ﬁnd our best-performing system is vastly preferred by judges over a traditional extractive summarization approach. This work motivates an entirely new approach to summarization, incorporating both sentiment analysis and item extraction for modernized, at-a-glance presentation of public opinion.

PDF Details