Stella Biderman


2022

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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.

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GPT-NeoX-20B: An Open-Source Autoregressive Language Model
Sidney Black | Stella Biderman | Eric Hallahan | Quentin Anthony | Leo Gao | Laurence Golding | Horace He | Connor Leahy | Kyle McDonell | Jason Phang | Michael Pieler | Usvsn Sai Prashanth | Shivanshu Purohit | Laria Reynolds | Jonathan Tow | Ben Wang | Samuel Weinbach
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge, the largest dense autoregressive model that has publicly available weights at the time of submission. In this work, we describe GPT-NeoX-20B’s architecture and training, and evaluate its performance. We open-source the training and evaluation code, as well as the model weights, at https://github.com/EleutherAI/gpt-neox.

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Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
Julia Kreutzer | Isaac Caswell | Lisa Wang | Ahsan Wahab | Daan van Esch | Nasanbayar Ulzii-Orshikh | Allahsera Tapo | Nishant Subramani | Artem Sokolov | Claytone Sikasote | Monang Setyawan | Supheakmungkol Sarin | Sokhar Samb | Benoît Sagot | Clara Rivera | Annette Rios | Isabel Papadimitriou | Salomey Osei | Pedro Ortiz Suarez | Iroro Orife | Kelechi Ogueji | Andre Niyongabo Rubungo | Toan Q. Nguyen | Mathias Müller | André Müller | Shamsuddeen Hassan Muhammad | Nanda Muhammad | Ayanda Mnyakeni | Jamshidbek Mirzakhalov | Tapiwanashe Matangira | Colin Leong | Nze Lawson | Sneha Kudugunta | Yacine Jernite | Mathias Jenny | Orhan Firat | Bonaventure F. P. Dossou | Sakhile Dlamini | Nisansa de Silva | Sakine Çabuk Ballı | Stella Biderman | Alessia Battisti | Ahmed Baruwa | Ankur Bapna | Pallavi Baljekar | Israel Abebe Azime | Ayodele Awokoya | Duygu Ataman | Orevaoghene Ahia | Oghenefego Ahia | Sweta Agrawal | Mofetoluwa Adeyemi
Transactions of the Association for Computational Linguistics, Volume 10

With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At least 15 corpora have no usable text, and a significant fraction contains less than 50% sentences of acceptable quality. In addition, many are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-proficient speakers, and supplement the human audit with automatic analyses. Finally, we recommend techniques to evaluate and improve multilingual corpora and discuss potential risks that come with low-quality data releases.

2021

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Towards a Model-Theoretic View of Narratives
Louis Castricato | Stella Biderman | David Thue | Rogelio Cardona-Rivera
Proceedings of the Third Workshop on Narrative Understanding

In this paper, we propose the beginnings of a formal framework for modeling narrative qua narrative. Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader’s story model over time, and Reader uncertainty. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader, along with two novel measurements of story coherence.