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VIDEO DOI: https://doi.org/10.48448/555q-r389

poster

ACL 2024

August 14, 2024

Bangkok, Thailand

Chain-of-Quizzes: Pedagogy-inspired Example Selection in In-Context-Learning

keywords:

example selection

in context learning

large language model

In-context learning (ICL) has emerged as a powerful tool for enhancing large language models (LLMs) in addressing downstream tasks. In this paper, we explore the vital task of example selection in ICL by mimicking the human learning process. We propose a Chain-of-Quizzes (CoQ) framework inspired by educational theories such as Bruner's Spiral Learning and Mastery Learning theory. Specifically, our framework employs the LLMs to answer the quiz (question in the example) to sift `good' examples, combines these examples iteratively with the increasing complexity, and utilizes a final exam to gauge the combined example chains. Our extensive experiments on diverse reasoning datasets show the proposed approach outperforms baseline models. These findings underscore the framework's potential for future research.

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