Papers by Guido Ivetta

7 papers
Selectively Answering Visual Questions (2024.findings-acl)

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Challenge: Large multi-modal models (LMMs) are capable of visual question answering (VQA) with unprecedented accuracy.
Approach: They propose a calibration score that can be used to quantify uncertainty in visual question answering models.
Outcome: The proposed calibration score is better calibrated than in text-only models for in-context learning.
Adaptive Data Collection for Latin-American Community-sourced Evaluation of Stereotypes (LACES) (2026.findings-acl)

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Challenge: a geo-cultural gap in NLP evaluation hinders evaluation of societal biases . authors propose a new method to collect stereotypes from large language models .
Approach: They propose a new method that integrates sourcing and validation of existing data into a single workflow.
Outcome: The proposed method improves LACES by integrating new stereotype entries and validation of existing data.
La Leaderboard: A Large Language Model Leaderboard for Spanish Varieties and Languages of Spain and Latin America (2025.acl-long)

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Challenge: La Leaderboard is the first open-source leaderboard to evaluate generative Large Language Models (LLMs) in languages and language varieties of Spain and Latin America.
Approach: They propose to use La Leaderboard to evaluate generative Large Language Models in Spanish and Latin America.
Outcome: La Leaderboard is the first open-source leaderboard to evaluate generative LLMs in languages and language varieties of Spain and Latin America.
Navigating Ethical Challenges in NLP: Hands-on strategies for students and researchers (2025.acl-tutorials)

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Challenge: This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues . participants will gain practical experience on when to flag a paper for ethics review .
Approach: This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues . participants will gain practical experience on when to flag a paper for ethics review .
Outcome: This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues . participants will gain practical experience on when to flag a paper for ethics review .
HESEIA: A community-based dataset for evaluating social biases in large language models, co-designed in real school settings in Latin America (2025.emnlp-main)

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Challenge: a dataset of 46,499 sentences created in a professional development course captures intersectional biases across multiple demographic axes and school subjects.
Approach: They present a large-scale dataset of 46,499 sentences created in a professional development course . they show that the dataset contains more stereotypes unrecognized by current LLMs .
Outcome: The proposed dataset captures intersectional biases across multiple demographic axes and school subjects.
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)

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Challenge: Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America.
Approach: They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts.
Outcome: The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories .

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