Guideline Learning for In-context Information Extraction

• URL: http://arxiv.org/abs/2310.05066v2
• Date: Sat, 21 Oct 2023 10:21:48 GMT
• Title: Guideline Learning for In-context Information Extraction
• Authors: Chaoxu Pang, Yixuan Cao, Qiang Ding, Ping Luo
• Abstract summary: In-context Information Extraction (IE) has recently garnered attention in the research community. We highlight a key reason for this shortfall: underspecified task description. We propose a Guideline Learning framework for In-context IE which reflectively learns and follows guidelines.
• Score: 29.062173997909028
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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