Fast Fine‐Tuning Large Language Models for Aspect‐Based Sentiment Analysis

ABSTRACT The method proposed in this study aims to reduce the execution time required for fine‐tuning large language models in aspect‐based sentiment analysis. To achieve efficient fine‐tuning, the large‐language model parameter tuning for new data is accelerated through rank decomposition. Experiments on the SemEval datasets demonstrated that our method consistently outperformed strong baselines such as GPT‐ABSA and BART‐ABSA across multiple metrics including accuracy, F1‐score, precision, and recall while also reducing fine‐tuning time by approximately 35%. The experimental results demonstrate a notable decrease in execution time with the proposed approach of the fine‐tuning process while preserving the accuracy of polarity prediction.