Papers by Pedro Henrique Luz de Araujo
Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance (2025.emnlp-main)
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| Challenge: | Prior work on persona prompting has shown mixed results on its effectiveness . prior work did not consider when and why personas should affect performance . |
| Approach: | They analyze literature on persona prompting and distill three desiderata for their effectiveness . they propose mitigation strategies to improve robustness but find they only work for the largest, most capable models . |
| Outcome: | The authors find that expert personas usually lead to positive or non-significant performance changes . they propose mitigation strategies to improve robustness but only for the largest models . |
VICTOR: a Dataset for Brazilian Legal Documents Classification (2020.lrec-1)
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Pedro Henrique Luz de Araujo, Teófilo Emídio de Campos, Fabricio Ataides Braz, Nilton Correia da Silva
| Challenge: | Approximately 10% of these are unstructured and requiring a lot of time to sort through. |
| Approach: | They propose to use a dataset built from Brazil's Supreme Court digitalized legal documents to improve document type classification and theme assignment tasks. |
| Outcome: | The proposed dataset is based on 45 thousand appeals and contains roughly 692 thousand documents—about 4.6 million pages. |
Cross-functional Analysis of Generalization in Behavioral Learning (2023.tacl-1)
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| Challenge: | Existing evaluation paradigms for behavioral learning use correlations in training data, but they ignore important model properties such as fairness. |
| Approach: | They propose an analysis method for evaluating behavioral learning considering generalization across dimensions of different granularity levels. |
| Outcome: | The proposed method optimizes behavior-specific loss functions and evaluates models on several partitions of the behavioral test suite controlled to leave out specific phenomena. |
Persistent Personas? Role-Playing, Instruction Following, and Safety in Extended Interactions (2026.eacl-long)
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| Challenge: | Persona-assigned large language models are used in education, healthcare and sociodemographic simulations. |
| Approach: | They propose a protocol that combines long persona dialogues and evaluation datasets to create dialogue-conditioned benchmarks that can robustly measure long-context effects. |
| Outcome: | The proposed protocol can measure persona fidelity, instruction-following, and safety in long conversations. |