@inproceedings{dong-etal-2024-survey,
title = "A Survey on In-context Learning",
author = "Dong, Qingxiu and
Li, Lei and
Dai, Damai and
Zheng, Ce and
Ma, Jingyuan and
Li, Rui and
Xia, Heming and
Xu, Jingjing and
Wu, Zhiyong and
Chang, Baobao and
Sun, Xu and
Li, Lei and
Sui, Zhifang",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.64",
doi = "10.18653/v1/2024.emnlp-main.64",
pages = "1107--1128",
abstract = "With the increasing capabilities of large language models (LLMs), in-context learning (ICL) has emerged as a new paradigm for natural language processing (NLP), where LLMs make predictions based on contexts augmented with a few examples. It has been a significant trend to explore ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress and challenges of ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques, including training strategies, prompt designing strategies, and related analysis. Additionally, we explore various ICL application scenarios, such as data engineering and knowledge updating. Finally, we address the challenges of ICL and suggest potential directions for further research. We hope that our work can encourage more research on uncovering how ICL works and improving ICL.",
}
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%0 Conference Proceedings
%T A Survey on In-context Learning
%A Dong, Qingxiu
%A Li, Lei
%A Dai, Damai
%A Zheng, Ce
%A Ma, Jingyuan
%A Li, Rui
%A Xia, Heming
%A Xu, Jingjing
%A Wu, Zhiyong
%A Chang, Baobao
%A Sun, Xu
%A Sui, Zhifang
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F dong-etal-2024-survey
%X With the increasing capabilities of large language models (LLMs), in-context learning (ICL) has emerged as a new paradigm for natural language processing (NLP), where LLMs make predictions based on contexts augmented with a few examples. It has been a significant trend to explore ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress and challenges of ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques, including training strategies, prompt designing strategies, and related analysis. Additionally, we explore various ICL application scenarios, such as data engineering and knowledge updating. Finally, we address the challenges of ICL and suggest potential directions for further research. We hope that our work can encourage more research on uncovering how ICL works and improving ICL.
%R 10.18653/v1/2024.emnlp-main.64
%U https://aclanthology.org/2024.emnlp-main.64
%U https://doi.org/10.18653/v1/2024.emnlp-main.64
%P 1107-1128
Markdown (Informal)
[A Survey on In-context Learning](https://aclanthology.org/2024.emnlp-main.64) (Dong et al., EMNLP 2024)
ACL
- Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, and Zhifang Sui. 2024. A Survey on In-context Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1107–1128, Miami, Florida, USA. Association for Computational Linguistics.