LLaMA-Based Models for Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2508.08649v1
• Date: Tue, 12 Aug 2025 05:30:32 GMT
• Title: LLaMA-Based Models for Aspect-Based Sentiment Analysis
• Authors: Jakub Šmíd, Pavel Přibáň, Pavel Král,
• Abstract summary: Large language models (LLMs) show promise for various tasks, their performance in compound aspect-based sentiment analysis (ABSA) tasks lags behind fine-tuned models.<n>This paper examines the capabilities of open-source LLMs fine-tuned for ABSA, focusing on LLaMA-based models.<n>We evaluate the performance across four tasks and eight English datasets, finding that the fine-tuned Orca2 model surpasses state-of-the-art results in all tasks.
• Score: 0.8602553195689511
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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