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Fine-tuned BERTurk outperforms LLMs in Turkish sentiment analysis

By PulseAugur Editorial · [2 sources] ·

A new study investigates the effectiveness of fine-tuning versus prompting large language models (LLMs) for Turkish sentiment analysis. The research found that fine-tuned BERTurk models outperformed prompted LLMs on a three-class sentiment classification task, particularly when dealing with neutral reviews. The findings indicate that while LLMs show promise, supervised fine-tuning remains crucial for robust sentiment analysis, especially when a neutral category is included. AI

IMPACT Fine-tuning remains a critical technique for specialized NLP tasks, even with the rise of large language models.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM performance.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Fine-tuned BERTurk outperforms LLMs in Turkish sentiment analysis

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sercan Karaka\c{s}, Yusuf \c{S}im\c{s}ek ·

    Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model

    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 pro…

  2. arXiv cs.CL TIER_1 English(EN) · Yusuf Şimşek ·

    Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model

    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-commerc…

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