Beatriz Maria Borges Ribeiro

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Related publications (4)

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

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

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

PEACOK: Persona Commonsense Knowledge for Consistent and Engaging Narratives

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

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

REFINER: Reasoning Feedback on Intermediate Representations

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

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

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

Maxime Jean Julien Peyrard, Robert West, 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

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