MEKiT：面向情感-原因对抽取的基于指令微调的多源异构知识注入方法
MEKiT: Multi-source Heterogeneous Knowledge Injection Method via Instruction Tuning for Emotion-Cause Pair Extraction
AI总结：
针对大语言模型在情感-原因对抽取任务中因缺乏辅助知识而表现不佳的问题，提出多源异构知识注入方法MEKiT，通过指令模板与数据混合的指令微调融合内部情感与外部因果知识，显著提升模型性能。
摘要：
尽管大语言模型（LLMs）在文本理解和生成方面表现出色，但它们在需要推理能力的情感-原因对抽取（ECPE）任务上的表现往往不如较小的语言模型。主要原因在于缺乏辅助知识，这限制了大语言模型有效感知情感和推理原因的能力。为解决这一问题，我们提出了一种新颖的多源异构知识注入方法MEKiT，该方法融合了异构的内部情感知识和外部因果知识。具体而言，针对这两类不同方面和结构的知识，我们分别采用融入指令模板和混合数据进行指令微调的方法，从而分别帮助大语言模型更全面地识别情感并更准确地推理原因。实验结果表明，MEKiT为ECPE任务提供了一种更有效且更具适应性的解决方案，相较于对比基线展现出绝对性能优势，并显著提升了大语言模型在ECPE任务上的表现。
英文摘要：
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.
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。
