Papers by Noa Garcia

2 papers
Attending Self-Attention: A Case Study of Visually Grounded Supervision in Vision-and-Language Transformers (2021.acl-srw)

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Challenge: a growing body of research has been focused on what attention heads learn during the pre-training of visual grounded language models.
Approach: They propose to use visual grounding to supervise attention directly to learn visual ground.
Outcome: The proposed method improves the performance of a state-of-the-art visual grounded language model on vision-and-language tasks.
Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs? (2024.lrec-main)

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Challenge: Multiple-choice questions (MCQs) are widely used in the evaluation of large language models (LLMs) however, there are concerns about whether MCQ can truly measure LLM’s capabilities.
Approach: They propose to use multiple choice questions to evaluate large language models (LLMs) to assess their capabilities.
Outcome: The proposed methods show that MCQs are less reliable than LFGQs in terms of expected calibration error.

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