用双视角框架提升大模型隐喻识别的透明度与准确性
A Dual-Perspective Metaphor Detection Framework Using Large Language Models
- 结合隐含与显式隐喻理论，引导大模型推理
- 自检机制验证回答，提升预测可靠性
- 在多个数据集上达顶尖性能，适合可解释性研究
隐喻检测是自然语言处理中的关键任务，旨在判断句子中某个词是否被用作隐喻。传统方法依赖监督学习模型，通过隐喻理论隐式编码语义关系，但决策过程缺乏透明性，影响预测可信度。近期研究表明大语言模型（LLMs）在隐喻检测方面具有潜力，但其推理受限于预定义知识图谱。为此，我们提出DMD框架，融合隐喻理论的隐含与显式应用，指导大模型进行隐喻检测，并引入自检机制验证引导所得结果。相比以往方法，该框架提供更透明的推理过程，且预测更可靠。实验表明，DMD在多个主流数据集上均取得领先性能。
原文摘要 · Abstract (English)
Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches often rely on supervised learning models that implicitly encode semantic relationships based on metaphor theories. However, these methods often suffer from a lack of transparency in their decision-making processes, which undermines the reliability of their predictions. Recent research indicates that LLMs (large language models) exhibit significant potential in metaphor detection. Nevertheless, their reasoning capabilities are constrained by predefined knowledge graphs. To overcome these limitations, we propose DMD, a novel dual-perspective framework that harnesses both implicit and explicit applications of metaphor theories to guide LLMs in metaphor detection and adopts a self-judgment mechanism to validate the responses from the aforementioned forms of guidance. In comparison to previous methods, our framework offers more transparent reasoning processes and delivers more reliable predictions. Experimental results prove the effectiveness of DMD, demonstrating state-of-the-art performance across widely-used datasets.
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