Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions

• URL: http://arxiv.org/abs/2407.02028v1
• Date: Tue, 2 Jul 2024 07:52:30 GMT
• Title: Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
• Authors: Xiang Li, Haoran Tang, Siyu Chen, Ziwei Wang, Ryan Chen, Marcin Abram,
• Abstract summary: We measure the performance of in-context learning as a function of task novelty and difficulty for open and closed questions. We show that counter-intuitively, a context that is more aligned with the topic does not always help more than a less relevant context.
• Score: 14.999106867218572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We measure the performance of in-context learning as a function of task novelty and difficulty for open and closed questions. For that purpose, we created a novel benchmark consisting of hard scientific questions, each paired with a context of various relevancy. We show that counter-intuitively, a context that is more aligned with the topic does not always help more than a less relevant context. This effect is especially visible for open questions and questions of high difficulty or novelty. This result reveals a fundamental difference between the treatment of close-form and open-form questions by large-language models and shows a need for a more robust evaluation of in-context learning on the variety of different types of questions. It also poses a new question of how to optimally select a context for large language models, especially in the context of Retrieval Augmented Generation (RAG) systems. Our results suggest that the answer to this question can be highly application-dependent and might be contingent on factors including the format of the question, the perceived difficulty level of the questions, and the novelty or popularity of the information we seek.

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