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This study investigates the effectiveness of fine-tuned open-source large language models (LLMs) for aspect-based sentiment analysis (ABSA) in low-resource environments, comparing their performance to state-of-the-art methods across various tasks and languages. The results indicate that fine-tuned LLMs outperform traditional models, especially in scenarios with limited training data, and achieve new state-of-the-art results on specific datasets. The findings highlight the adaptability and robustness of fine

Author

ismailifakir5

Language

EN