Papers by Anaelia Ovalle
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies (2021.emnlp-main)
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| Challenge: | Recent work analyzes, quantifies, and mitigates language model biases such as gender, race or religion-related stereotypes in static word embeddings and contextual representations. |
| Approach: | They explain the complexity of gender and language around it and examine how current representations perpetuate harms associated with binary gender. |
| Outcome: | The proposed model and dataset biases perpetuate harms associated with the treatment of gender as binary in English language technologies. |
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies (2024.findings-naacl)
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Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta
| Challenge: | a recent study documented the harmful limitations of gender binary-centric large language models . data scarcity is a known culprit, but the precise mechanisms through which scarcity affects this behavior remain underexplored. |
| Approach: | They propose to use BPE tokenization to enforce consistent tokenization across gendered pronouns to improve neopronoun proficiency. |
| Outcome: | The proposed methods outperform finetuning with standard BPE, and improve neopronoun proficiency. |
SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models (2025.naacl-long)
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Margaret Mitchell, Giuseppe Attanasio, Ioana Baldini, Miruna Clinciu, Jordan Clive, Pieter Delobelle, Manan Dey, Sil Hamilton, Timm Dill, Jad Doughman, Ritam Dutt, Avijit Ghosh, Jessica Zosa Forde, Carolin Holtermann, Lucie-Aimée Kaffee, Tanmay Laud, Anne Lauscher, Roberto L Lopez-Davila, Maraim Masoud, Nikita Nangia, Anaelia Ovalle, Giada Pistilli, Dragomir Radev, Beatrice Savoldi, Vipul Raheja, Jeremy Qin, Esther Ploeger, Arjun Subramonian, Kaustubh Dhole, Kaiser Sun, Amirbek Djanibekov, Jonibek Mansurov, Kayo Yin, Emilio Villa Cueva, Sagnik Mukherjee, Jerry Huang, Xudong Shen, Jay Gala, Hamdan Al-Ali, null Tair Djanibekov, Nurdaulet Mukhituly, Shangrui Nie, Shanya Sharma, Karolina Stanczak, Eliza Szczechla, Tiago Timponi Torrent, Deepak Tunuguntla, Marcelo Viridiano, Oskar Van Der Wal, Adina Yakefu, Aurélie Névéol, Mike Zhang, Sydney Zink, Zeerak Talat
| Challenge: | Large Language Models reproduce and exacerbate social biases present in training data, and resources to quantify this issue are limited. |
| Approach: | They propose a multilingual parallel dataset to examine culturally-specific stereotypes that may be learned by LLMs. |
| Outcome: | The proposed dataset includes stereotypes from 20 regions around the world and 16 languages, spanning multiple identity categories subject to discrimination worldwide. |