Beatriz Maria Borges Ribeiro

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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 ...

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 ...

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 ...

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 ...

Beatriz Maria Borges Ribeiro

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

REFINER: Reasoning Feedback on Intermediate Representations

PEACOK: Persona Commonsense Knowledge for Consistent and Engaging Narratives

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