arXiv:2606.29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models. We compare classical machine learning methods, fine-tuned pretrained language models, and prompted large language models on a Turkish e-commerce review dataset with negative, neutral, and positive labels. Fine-tuned BERTurk models perform best overall and outperform all prompted large language models in the full three-class task. The neutral class emerges as the main difficulty: while several large language mo
The proliferation of Large Language Models (LLMs) and the increasing focus on their performance across diverse languages necessitate ongoing evaluation of traditional fine-tuning approaches.
This study provides a concrete data point on the continued relevance of fine-tuning for specific language tasks even in the presence of powerful LLMs, influencing deployment strategies and research directions.
The perceived 'all-encompassing' ability of LLMs is nuanced by findings that fine-tuned, smaller models can still outperform them on specific, linguistically complex tasks like Turkish sentiment analysis, especially for nuanced categories.
- · Machine learning researchers focusing on language-specific optimizations
- · Developers of specialized language models and fine-tuning techniques
- · Companies requiring high-accuracy sentiment analysis in non-English languages
- · Teams solely relying on zero-shot or few-shot prompting of large general-purpose
- · Companies underestimating linguistic nuances in LLM deployment
Increased investment in developing and fine-tuning domain-specific or language-specific models rather than solely scaling general LLMs.
Demand for more efficient and automated fine-tuning platforms to make specialized models more accessible for non-English languages.
The development of hybrid AI architectures that strategically combine advanced prompting with fine-tuned components for optimal performance across complex linguistic tasks.
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Read at arXiv cs.AI