ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation
This work addresses the challenge of adapting LLMs to specialized domains like computational argumentation, which is incremental as it applies existing fine-tuning methods to a new domain.
The authors tackled the problem of large language models struggling with domain-specific tasks by introducing specialized instruction fine-tuning for computational argumentation, resulting in significant performance improvements on both seen and unseen CA tasks while maintaining generalization on general NLP benchmarks.
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.