用大模型零样本学习实现帧语义解析，无需微调即可达高准确率。
Exploring In-Context Learning for Frame-Semantic Parsing
- 基于FrameNet自动生成任务提示，无需人工设计
- 帧识别F1达94.3%，语义角色标注77.4%
- 适合无标注数据的领域专用语义解析场景
帧语义解析（FSP）旨在识别谓词并依据帧语义标注其论元。本文研究使用大语言模型（LLM）的上下文学习（ICL）方法，在不进行模型微调的前提下完成FSP。提出一种自动构建任务相关提示的方法，仅依赖FrameNet数据库，利用框架定义和标注样例生成用于帧识别（FI）和帧语义角色标注（FSRL）子任务的提示。在与暴力事件相关的帧子集上测试，该方法在六种不同LLM上取得良好效果：FI的F1为94.3%，FSRL为77.4%。结果表明，ICL为特定领域FSP任务提供了一种实用且有效的替代微调的方案。
原文摘要 · Abstract (English)
Frame Semantic Parsing (FSP) entails identifying predicates and labeling their arguments according to Frame Semantics. This paper investigates the use of In-Context Learning (ICL) with Large Language Models (LLMs) to perform FSP without model fine-tuning. We propose a method that automatically generates task-specific prompts for the Frame Identification (FI) and Frame Semantic Role Labeling (FSRL) subtasks, relying solely on the FrameNet database. These prompts, constructed from frame definitions and annotated examples, are used to guide six different LLMs. Experiments are conducted on a subset of frames related to violent events. The method achieves competitive results, with F1 scores of 94.3% for FI and 77.4% for FSRL. The findings suggest that ICL offers a practical and effective alternative to traditional fine-tuning for domain-specific FSP tasks.
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