大语言模型从演示中学习任务启发式：一种面向文档级事件论元抽取的启发式驱动提示策略
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction
AI总结：
本文提出启发式驱动的类比链接提示策略HD-LoA，通过显式构建任务启发式演示和类比推理，提升大语言模型在文档级事件论元抽取及多种NLP任务上的少样本性能。
摘要：
在本研究中，我们探究文档级事件论元抽取（EAE）中的上下文学习（ICL），以减轻该任务对大规模标注数据的依赖。我们提出启发式驱动的类比链接（HD-LoA）提示方法，以应对示例选择的挑战，并开发一种专门针对EAE的提示策略。具体而言，我们假设并验证了大语言模型通过ICL从演示中学习任务特定启发式。基于这一假设，我们引入一种显式的启发式驱动演示构建方法，将随意的示例选择过程转变为强调任务启发式的系统化方法。此外，受人类类比推理的启发，我们提出类比链接提示，使大语言模型能够通过类比已知情境来处理新情境，从而在有限的ICL示例之外提升其对未见类别的性能。实验表明，我们的方法在文档级EAE数据集上优于现有提示方法和少样本监督学习方法。此外，HD-LoA提示在情感分析和自然语言推理等多种任务中表现出有效性，展示了其广泛的适应性。
英文摘要：
In this study, we investigate in-context learning (ICL) in document-level event argument extraction (EAE) to alleviate the dependency on large-scale labeled data for this task. We introduce the Heuristic-Driven Link-of-Analogy (HD-LoA) prompting to address the challenge of example selection and to develop a prompting strategy tailored for EAE. Specifically, we hypothesize and validate that LLMs learn task-specific heuristics from demonstrations via ICL. Building upon this hypothesis, we introduce an explicit heuristic-driven demonstration construction approach, which transforms the haphazard example selection process into a methodical method that emphasizes task heuristics. Additionally, inspired by the analogical reasoning of human, we propose the link-of-analogy prompting, which enables LLMs to process new situations by drawing analogies to known situations, enhancing their performance on unseen classes beyond limited ICL examples. Experiments show that our method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets. Additionally, the HD-LoA prompting shows effectiveness in diverse tasks like sentiment analysis and natural language inference, demonstrating its broad adaptability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
