Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering

• URL: http://arxiv.org/abs/2212.10375v2
• Date: Wed, 3 May 2023 14:43:50 GMT
• Title: Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering
• Authors: Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, Lingpeng Kong
• Abstract summary: This paper advocates a new principle for in-context learning (ICL): self-adaptive in-context learning. The self-adaption mechanism is introduced to help each sample find an in-context example permutation that can derive the correct prediction. Our self-adaptive ICL method achieves a 40% relative improvement over the common practice setting.
• Score: 15.3566963926257
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context. This paper advocates a new principle for ICL: self-adaptive in-context learning. The self-adaption mechanism is introduced to help each sample find an in-context example permutation (i.e., selection and ordering) that can derive the correct prediction, thus maximizing performance. To validate the effectiveness of self-adaptive ICL, we propose a general select-then-rank framework and instantiate it with new selection and ranking algorithms. Upon extensive evaluation on eight different NLP datasets, our self-adaptive ICL method achieves a 40% relative improvement over the common practice setting. Further analysis reveals the enormous potential of self-adaptive ICL that it might be able to close the gap between ICL and finetuning given more advanced algorithms. Our code is released to facilitate future research in this area: https://github.com/Shark-NLP/self-adaptive-ICL

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