Using LoRA to Fine-tune Large Language Models for Analyzing Collaborative Argumentation in Classrooms
Deliang Wang, Chao Yang, Gaowei Chen · 2025
Artificial intelligence (AI) has been employed to provide automated analysis of collaborative argumentation due to its importance. However, traditional deep learning models face challenges with generalizability to other dimensions and contexts. Existing studies on large language models (LLMs) for classroom dialogue primarily rely on prompt engineering techniques because of the high costs associated with fully fine-tuning LLMs. This approach results in limited performance, indicating a need for improvement. To address these issues, this study proposes the use of parameter-efficient fine-tuning (PEFT) techniques to optimize the performance of LLMs in analyzing classroom collaborative argumentation. Specifically, we utilized Low-Rank Adaptation (LoRA), a well-known PEFT technique, to fine-tune two state-of-the-art LLMs, Llama-3.2-3B and Gemma-2-9B. The results demonstrate that, compared to fully fine-tuning BERT and RoBERTa, using LoRA for PEFT of Llama-3.2-3B and Gemma-2-9B achieves superior performance in analyzing argument moves within collaborative argumentation. We conclude that PEFT techniques provide a promising direction for classroom dialogue analysis.