用多源异构知识注入，让大模型更准地识别情绪与原因。
MEKiT: Multi-source Heterogeneous Knowledge Injection Method via Instruction Tuning for Emotion-Cause Pair Extraction
- 通过指令微调融合内部情绪知识和外部因果知识
- 在多个数据集上显著提升大模型的抽取准确率
- 适合需要强推理能力的情感分析任务
尽管大语言模型在文本理解与生成方面表现优异，但在需要推理能力的“情绪-原因对抽取”（ECPE）任务中，其表现常不如小型语言模型。主要原因是缺乏辅助知识，限制了大模型对情绪的感知和原因推理能力。为此，我们提出一种新型的多源异构知识注入方法MEKiT，整合内部情感知识与外部因果知识。针对两类知识在内容与结构上的差异，分别采用指令模板融入与数据混合的方式进行指令微调，从而提升大模型在情绪识别与原因推理方面的全面性与准确性。实验表明，MEKiT在多个基准数据集上均显著优于对比方法，大幅改善了大模型在ECPE任务中的性能。
原文摘要 · Abstract (English)
Although large language models (LLMs) excel in text comprehension and generation, their performance on the Emotion-Cause Pair Extraction (ECPE) task, which requires reasoning ability, is often underperform smaller language model. The main reason is the lack of auxiliary knowledge, which limits LLMs' ability to effectively perceive emotions and reason causes. To address this issue, we propose a novel \textbf{M}ulti-source h\textbf{E}terogeneous \textbf{K}nowledge \textbf{i}njection me\textbf{T}hod, MEKiT, which integrates heterogeneous internal emotional knowledge and external causal knowledge. Specifically, for these two distinct aspects and structures of knowledge, we apply the approaches of incorporating instruction templates and mixing data for instruction-tuning, which respectively facilitate LLMs in more comprehensively identifying emotion and accurately reasoning causes. Experimental results demonstrate that MEKiT provides a more effective and adaptable solution for the ECPE task, exhibiting an absolute performance advantage over compared baselines and dramatically improving the performance of LLMs on the ECPE task.
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