LLaMA-Based Models for Aspect-Based Sentiment Analysis
Abstract
While large language models (LLMs) show promise for various tasks, their performance in compound aspect-based sentiment analysis (ABSA) tasks lags behind fine-tuned models. However, the potential of LLMs fine-tuned for ABSA remains unexplored. This paper examines the capabilities of open-source LLMs fine-tuned for ABSA, focusing on LLaMA-based models. We evaluate the performance across four tasks and eight English datasets, finding that the fine-tuned Orca~2 model surpasses state-of-the-art results in all tasks. However, all models struggle in zero-shot and few-shot scenarios compared to fully fine-tuned ones. Additionally, we conduct error analysis to identify challenges faced by fine-tuned models.
Cite
@article{arxiv.2508.08649,
title = {LLaMA-Based Models for Aspect-Based Sentiment Analysis},
author = {Jakub Šmíd and Pavel Přibáň and Pavel Král},
journal= {arXiv preprint arXiv:2508.08649},
year = {2025}
}
Comments
Published in Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis (WASSA 2024). Official version: https://aclanthology.org/2024.wassa-1.6/