ArgInstruct：面向计算论证的专用指令微调
ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation
AI总结：
本研究提出面向计算论证领域的专用指令微调方法ArgInstruct，通过构建105个任务的CA基准和52k条指令训练模型，显著提升了LLM在已见和未见CA任务上的表现，同时保持通用NLP任务性能稳定。
摘要：
训练大型语言模型遵循指令已显著增强了它们处理未见任务的能力。然而，尽管具有强大的泛化能力，指令遵循型LLM在处理需要领域知识的任务时仍面临困难。本研究针对计算论证（CA）领域引入了一种专用指令微调方法。目标是使LLM能够有效处理任何未见CA任务，同时保持其泛化能力。为此，我们回顾了现有CA研究，为105个CA任务设计了自然语言指令。在此基础上，我们开发了针对LLM的CA专用基准，可全面评估LLM解决各类CA任务的能力。我们合成了52k条CA相关指令，并调整self-instruct流程来训练一个CA专用的指令遵循LLM。实验表明，CA专用指令微调显著提升了LLM在已见和未见CA任务上的表现。同时，在SuperNI基准的通用NLP任务上性能保持稳定。
英文摘要：
Training large language models (LLMs) to follow instructions has significantly enhanced their ability to tackle unseen tasks. However, despite their strong generalization capabilities, instruction-following LLMs encounter difficulties when dealing with tasks that require domain knowledge. This work introduces a specialized instruction fine-tuning for the domain of computational argumentation (CA). The goal is to enable an LLM to effectively tackle any unseen CA tasks while preserving its generalization capabilities. Reviewing existing CA research, we crafted natural language instructions for 105 CA tasks to this end. On this basis, we developed a CA-specific benchmark for LLMs that allows for a comprehensive evaluation of LLMs' capabilities in solving various CA tasks. We synthesized 52k CA-related instructions, adapting the self-instruct process to train a CA-specialized instruction-following LLM. Our experiments suggest that CA-specialized instruction fine-tuning significantly enhances the LLM on both seen and unseen CA tasks. At the same time, performance on the general NLP tasks of the SuperNI benchmark remains stable.
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。
