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Anaelia Ovalle

4 accepted papers

2025

SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models

NAACL 2025long

Large Language Models (LLMs) reproduce and exacerbate the social biases present in their training data, and resources to quantify this issue are limited. While research has attempted to identify and mitigate such biases, most efforts have been concentrated around English, lagging the rapid advanceme…

Cited by 1SourcePDFScholar
2024

Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies

NAACL 2024findings

Gender-inclusive NLP research has documented the harmful limitations of gender binary-centric large language models (LLM), such as the inability to correctly use gender-diverse English neopronouns (e.g., xe, zir, fae). While data scarcity is a known culprit, the precise mechanisms through which scar…

2023

Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract)

AAAI 2023technical

Intentionally crafted adversarial samples have effectively exploited weaknesses in deep neural networks. A standard method in adversarial robustness assumes a framework to defend against samples crafted by minimally perturbing a sample such that its corresponding model output changes. These sensitiv…

Cited by 0SourcePDFScholar
2021

Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies

EMNLP 2021main

Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities. These harms are driven…