Luiza Amador Pozzobon — accepted AI-conference papers

2026

LEGATO: Large-scale End-to-end Generalizable Approach to Typeset OMR

Guang Yang, Victoria Ebert, Nazif Can Tamer, Brian Siyuan Zheng, Luiza Amador Pozzobon, Noah A. Smith

ICLR 2026poster

We propose Legato, a new end-to-end model for optical music recognition (OMR), a task of converting music score images to machine-readable documents. Legato is the first large-scale pretrained OMR model capable of recognizing full-page or multi-page typeset music scores and the first to generate do…

Cited by 0 Source ↗code ↗Scholar ↗

2023

Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models

Luiza Amador Pozzobon, Beyza Ermis, Patrick Lewis, Sara Hooker

EMNLP 2023long findings

Considerable effort has been dedicated to mitigating toxicity, but existing methods often require drastic modifications to model parameters or the use of computationally intensive auxiliary models. Furthermore, previous approaches have often neglected the crucial factor of language's evolving nature…

Cited by 0 Source ↗code ↗Scholar ↗

2023

On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research

Luiza Amador Pozzobon, Beyza Ermis, Patrick Lewis, Sara Hooker

EMNLP 2023long main

Perception of toxicity evolves over time and often differs between geographies and cultural backgrounds. Similarly, black-box commercially available APIs for detecting toxicity, such as the Perspective API, are not static, but frequently retrained to address any unattended weaknesses and biases. We…

Cited by 0 Source ↗code ↗Scholar ↗