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        <title>Guideline Learning for In-Context Information Extraction</title>
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        <namePart type="given">Chaoxu</namePart>
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        <namePart type="given">Yixuan</namePart>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Large language models (LLMs) can perform a new task by merely conditioning on task instructions and a few input-output examples, without optimizing any parameters. This is called In-Context Learning (ICL). In-context Information Extraction (IE) has recently garnered attention in the research community. However, the performance of In-context IE generally lags behind the state-of-the-art supervised expert models. We highlight a key reason for this shortfall: underspecified task description. The limited-length context struggles to thoroughly express the intricate IE task instructions and various edge cases, leading to misalignment in task comprehension with humans. In this paper, we propose a Guideline Learning (GL) framework for In-context IE which reflectively learns and follows guidelines. During the learning phrase, GL automatically synthesizes a set of guidelines based on a few error cases, and during inference, GL retrieves helpful guidelines for better ICL. Moreover, we propose a self-consistency-based active learning method to enhance the efficiency of GL. Experiments on event extraction and relation extraction show that GL can significantly improve the performance of in-context IE.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.950</identifier>
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%0 Conference Proceedings
%T Guideline Learning for In-Context Information Extraction
%A Pang, Chaoxu
%A Cao, Yixuan
%A Ding, Qiang
%A Luo, Ping
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F pang-etal-2023-guideline
%X Large language models (LLMs) can perform a new task by merely conditioning on task instructions and a few input-output examples, without optimizing any parameters. This is called In-Context Learning (ICL). In-context Information Extraction (IE) has recently garnered attention in the research community. However, the performance of In-context IE generally lags behind the state-of-the-art supervised expert models. We highlight a key reason for this shortfall: underspecified task description. The limited-length context struggles to thoroughly express the intricate IE task instructions and various edge cases, leading to misalignment in task comprehension with humans. In this paper, we propose a Guideline Learning (GL) framework for In-context IE which reflectively learns and follows guidelines. During the learning phrase, GL automatically synthesizes a set of guidelines based on a few error cases, and during inference, GL retrieves helpful guidelines for better ICL. Moreover, we propose a self-consistency-based active learning method to enhance the efficiency of GL. Experiments on event extraction and relation extraction show that GL can significantly improve the performance of in-context IE.
%R 10.18653/v1/2023.emnlp-main.950
%U https://aclanthology.org/2023.emnlp-main.950
%U https://doi.org/10.18653/v1/2023.emnlp-main.950
%P 15372-15389

Markdown (Informal)

[Guideline Learning for In-Context Information Extraction](https://aclanthology.org/2023.emnlp-main.950) (Pang et al., EMNLP 2023)

• Guideline Learning for In-Context Information Extraction (Pang et al., EMNLP 2023)

ACL

• Chaoxu Pang, Yixuan Cao, Qiang Ding, and Ping Luo. 2023. Guideline Learning for In-Context Information Extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15372–15389, Singapore. Association for Computational Linguistics.