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Beatriz Maria Borges Ribeiro

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Related publications (4)

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Let Me Teach You: Pedagogical Foundations of Feedback for Language Models

Tanja Christina Käser Jacober, Antoine Bosselut, Beatriz Maria Borges Ribeiro

Natural Language Feedback (NLF) is an increasingly popular mechanism for aligning Large Language Models (LLMs) to human preferences. Despite the diversity of the information it can convey, NLF methods are often hand-designed and arbitrary, with little syst ...

Association for Computational Linguistics2024

REFINER: Reasoning Feedback on Intermediate Representations

Boi Faltings, Robert West, Antoine Bosselut, Maxime Jean Julien Peyrard, Debjit Paul, Mahammad Ismayilzada, Beatriz Maria Borges Ribeiro

Language models (LMs) have recently shown remarkable performance on reasoning tasks by explicitly generating intermediate inferences, e.g., chain-of-thought prompting. However, these intermediate inference steps may be in- appropriate deductions from the i ...

2023

PEACOK: Persona Commonsense Knowledge for Consistent and Engaging Narratives

Soyoung Oh, Antoine Bosselut, Beatriz Maria Borges Ribeiro, Deniz Bayazit, Silin Gao

Sustaining coherent and engaging narratives requires dialogue or storytelling agents to understand how the personas of speakers or listeners ground the narrative. Specifically, these agents must infer personas of their listeners to produce statements that ...

Assoc Computational Linguistics-Acl2023

Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?

Robert West, Maxime Jean Julien Peyrard, Kristina Gligoric, Beatriz Maria Borges Ribeiro

The automatic detection of humor poses a grand challenge for natural language processing. Transformer-based systems have recently achieved remarkable results on this task, but they usually (1) were evaluated in setups where serious vs humorous texts came f ...

International Joint Conferences on Artificial Intelligence Organization2021