Papers by Beatriz Borges
Let Me Teach You: Pedagogical Foundations of Feedback for Language Models (2024.emnlp-main)
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| Challenge: | Natural Language Feedback (NLF) is an increasingly popular mechanism for aligning Large Language Models to human preferences. |
| Approach: | They propose a feedback framework for Large Language Models that outlines various characteristics of the feedback space and a taxonomy based on these variables. |
| Outcome: | The proposed framework provides a general mapping of the feedback space and provides examples for mapping to future research. |
PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives (2023.acl-long)
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Silin Gao, Beatriz Borges, Soyoung Oh, Deniz Bayazit, Saya Kanno, Hiromi Wakaki, Yuki Mitsufuji, Antoine Bosselut
| Challenge: | a new knowledge graph for personas based on human-validated persona facts is constructed to model diverse persona attributes . a variety of persona characteristics are required to sustain coherent narratives . |
| Approach: | They construct a large-scale persona commonsense knowledge graph with 100K human-validated persona facts. |
| Outcome: | The proposed graph contains rich and precise world persona inferences that help systems generate more consistent and engaging narratives. |
REFINER: Reasoning Feedback on Intermediate Representations (2024.eacl-long)
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Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, Boi Faltings
| Challenge: | Language models (LLMs) have shown remarkable performance by explicitly generating intermediate inferences,e.g., chain-of-thought prompting. |
| Approach: | They propose a framework for finetuning LMs to generate intermediate reasoning steps while interacting with a critic model that provides automated feedback on the reasoning. |
| Outcome: | Empirical evaluations of REFINER on three diverse reasoning tasks show that it significantly improves over baseline models. |
CAVE : Detecting and Explaining Commonsense Anomalies in Visual Environments (2025.emnlp-main)
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Rishika Bhagwatkar, Syrielle Montariol, Angelika Romanou, Beatriz Borges, Irina Rish, Antoine Bosselut
| Challenge: | a new benchmark for computer vision fails to capture richness and unpredictability of real-world anomalies . state-of-the-art VLMs struggle with visual anomaly perception and commonsense reasoning . elucidating the nature of anomalies is a fundamental human trait . |
| Approach: | They propose a benchmark for visual anomalies that includes annotations for visual grounding and categorizing anomalies based on their visual manifestations, their complexity, severity, and commonness. |
| Outcome: | The proposed benchmark improves on existing vision models by incorporating visual annotations. |