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.- Anthology ID:
- 2024.wassa-1.6
- Volume:
- Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Orphée De Clercq, Valentin Barriere, Jeremy Barnes, Roman Klinger, João Sedoc, Shabnam Tafreshi
- Venues:
- WASSA | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 63–70
- Language:
- URL:
- https://aclanthology.org/2024.wassa-1.6
- DOI:
- Cite (ACL):
- Jakub Šmíd, Pavel Priban, and Pavel Kral. 2024. LLaMA-Based Models for Aspect-Based Sentiment Analysis. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 63–70, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- LLaMA-Based Models for Aspect-Based Sentiment Analysis (Šmíd et al., WASSA-WS 2024)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2024.wassa-1.6.pdf