@inproceedings{wu-etal-2023-self,
title = "Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering",
author = "Wu, Zhiyong and
Wang, Yaoxiang and
Ye, Jiacheng and
Kong, Lingpeng",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.79",
doi = "10.18653/v1/2023.acl-long.79",
pages = "1423--1436",
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 organization (i.e., selection and permutation) 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 will be released to facilitate future research.",
}
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<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 organization (i.e., selection and permutation) 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 will be released to facilitate future research.</abstract>
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%0 Conference Proceedings
%T Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering
%A Wu, Zhiyong
%A Wang, Yaoxiang
%A Ye, Jiacheng
%A Kong, Lingpeng
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wu-etal-2023-self
%X 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 organization (i.e., selection and permutation) 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 will be released to facilitate future research.
%R 10.18653/v1/2023.acl-long.79
%U https://aclanthology.org/2023.acl-long.79
%U https://doi.org/10.18653/v1/2023.acl-long.79
%P 1423-1436
Markdown (Informal)
[Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering](https://aclanthology.org/2023.acl-long.79) (Wu et al., ACL 2023)
ACL