LLaMA-Based Models for Aspect-Based Sentiment Analysis
This work addresses the problem of improving sentiment analysis accuracy for researchers and practitioners in NLP, though it is incremental as it builds on existing fine-tuning methods.
The paper tackled the underperformance of large language models in aspect-based sentiment analysis by fine-tuning LLaMA-based models, finding that the fine-tuned Orca~2 model achieved state-of-the-art results across all evaluated tasks and datasets.
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.