基于关键词中心的提示方法及自生成理由增强的一次性事件检测
Keyword-Centric Prompting for One-Shot Event Detection with Self-Generated Rationale Enhancements
AI总结：
针对LLM在事件检测中因缺乏触发词理解和过度解读导致ICL失效的问题，提出KeyCP++方法，通过构建触发词判别模板生成理由，减少对关键词依赖并促进规则学习，显著提升一次性事件检测性能。
摘要：
尽管基于LLM的上下文学习（ICL）范式在各种自然语言处理任务中取得了显著成功，但在事件检测中却面临挑战。这是因为LLM对事件触发词缺乏准确理解，且容易过度解读，仅通过上下文示例无法有效纠正。本文聚焦于最具挑战性的一次性设置，提出了一种以关键词为中心的思维链提示方法KeyCP++。KeyCP++通过自动标注演示中输入文本与检测结果之间的逻辑空白，解决了传统ICL的缺陷。具体而言，为生成深入且有意义的理由，KeyCP++构建了触发词判别提示模板。它将示例触发词（即关键词）作为锚点纳入提示，以简化触发词分析，让LLM提出候选触发词，并对每个候选进行论证。这种提出并判断的理由机制帮助LLM减少对关键词的过度依赖，促进检测规则学习。大量实验证明了该方法的有效性，在一次性事件检测中展现了显著进步。
英文摘要：
Although the LLM-based in-context learning (ICL) paradigm has demonstrated considerable success across various natural language processing tasks, it encounters challenges in event detection. This is because LLMs lack an accurate understanding of event triggers and tend to make over-interpretation, which cannot be effectively corrected through in-context examples alone. In this paper, we focus on the most challenging one-shot setting and propose KeyCP++, a keyword-centric chain-of-thought prompting approach. KeyCP++ addresses the weaknesses of conventional ICL by automatically annotating the logical gaps between input text and detection results for the demonstrations. Specifically, to generate in-depth and meaningful rationale, KeyCP++ constructs a trigger discrimination prompting template. It incorporates the exemplary triggers (a.k.a keywords) into the prompt as the anchor to simply trigger profiling, let LLM propose candidate triggers, and justify each candidate. These propose-and-judge rationales help LLMs mitigate over-reliance on the keywords and promote detection rule learning. Extensive experiments demonstrate the effectiveness of our approach, showcasing significant advancements in one-shot event detection.
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。
