用关键词引导推理链，提升大模型在单样本事件检测中的准确率
Keyword-Centric Prompting for One-Shot Event Detection with Self-Generated Rationale Enhancements
- 基于关键词构建推理模板，引导模型自动生成判断理由
- 在单样本设置下，事件检测精确率提升显著，优于传统提示方法
- 适合需要少样本高效检测的场景，如突发事件监测
尽管基于大语言模型的上下文学习在自然语言处理中表现优异，但在事件检测任务中仍面临挑战，因模型对事件触发词理解不准确，易产生过度解读，仅靠示例难以纠正。本文聚焦最困难的单样本设置，提出关键词中心的推理链提示方法KeyCP++。该方法通过自动标注示范样本中输入与检测结果间的逻辑断层，构建触发词区分提示模板，将示例中的触发词（关键词）作为锚点，引导模型生成候选触发词并逐个验证，形成‘提出-判断’式推理过程。这一机制有效缓解了对关键词的过度依赖，促进检测规则学习。大量实验表明，该方法在单样本事件检测上实现显著性能提升。
原文摘要 · Abstract (English)
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.
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