Sasha Luccioni


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Towards Reproducible Machine Learning Research in Natural Language Processing
Ana Lucic | Maurits Bleeker | Samarth Bhargav | Jessica Forde | Koustuv Sinha | Jesse Dodge | Sasha Luccioni | Robert Stojnic
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility. Despite proposals such as the Reproducibility Checklist and reproducibility criteria at several major conferences, the reflex for carrying out research with reproducibility in mind is lacking in the broader ML community. We propose this tutorial as a gentle introduction to ensuring reproducible research in ML, with a specific emphasis on computational linguistics and NLP. We also provide a framework for using reproducibility as a teaching tool in university-level computer science programs.

Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
Leandro Von Werra | Lewis Tunstall | Abhishek Thakur | Sasha Luccioni | Tristan Thrush | Aleksandra Piktus | Felix Marty | Nazneen Rajani | Victor Mustar | Helen Ngo
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Evaluation is a key part of machine learning (ML), yet there is a lack of support and tooling to enable its informed and systematic practice. We introduce Evaluate and Evaluation on the Hub—a set of tools to facilitate the evaluation of models and datasets in ML. Evaluate is a library to support best practices for measurements, metrics, and comparisons of data and models. Its goal is to support reproducibility of evaluation, centralize and document the evaluation process, and broaden evaluation to cover more facets of model performance. It includes over 50 efficient canonical implementations for a variety of domains and scenarios, interactive documentation, and the ability to easily share implementations and outcomes. The library is available at In addition, we introduce Evaluation on the Hub, a platform that enables the large-scale evaluation of over 75,000 models and 11,000 datasets on the Hugging Face Hub, for free, at the click of a button. Evaluation on the Hub is available at

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Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)
Angela Fan | Iryna Gurevych | Yufang Hou | Zornitsa Kozareva | Sasha Luccioni | Nafise Sadat Moosavi | Sujith Ravi | Gyuwan Kim | Roy Schwartz | Andreas Rücklé
Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)

You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings
Zeerak Talat | Aurélie Névéol | Stella Biderman | Miruna Clinciu | Manan Dey | Shayne Longpre | Sasha Luccioni | Maraim Masoud | Margaret Mitchell | Dragomir Radev | Shanya Sharma | Arjun Subramonian | Jaesung Tae | Samson Tan | Deepak Tunuguntla | Oskar Van Der Wal
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

Evaluating bias, fairness, and social impact in monolingual language models is a difficult task. This challenge is further compounded when language modeling occurs in a multilingual context. Considering the implication of evaluation biases for large multilingual language models, we situate the discussion of bias evaluation within a wider context of social scientific research with computational work.We highlight three dimensions of developing multilingual bias evaluation frameworks: (1) increasing transparency through documentation, (2) expanding targets of bias beyond gender, and (3) addressing cultural differences that exist between languages.We further discuss the power dynamics and consequences of training large language models and recommend that researchers remain cognizant of the ramifications of developing such technologies.