Are LLMs Ready to Replace Bangla Annotators?

• URL: http://arxiv.org/abs/2602.16241v2
• Date: Thu, 19 Feb 2026 05:23:43 GMT
• Title: Are LLMs Ready to Replace Bangla Annotators?
• Authors: Md. Najib Hasan, Touseef Hasan, Souvika Sarkar,
• Abstract summary: Large Language Models (LLMs) are increasingly used as automated annotators to scale dataset creation.<n>We study the behavior of LLMs as zero-shot annotators for Bangla hate speech.<n>Our analysis uncovers annotator bias and substantial instability in model judgments.
• Score: 0.5468559068505657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly used as automated annotators to scale dataset creation, yet their reliability as unbiased annotators--especially for low-resource and identity-sensitive settings--remains poorly understood. In this work, we study the behavior of LLMs as zero-shot annotators for Bangla hate speech, a task where even human agreement is challenging, and annotator bias can have serious downstream consequences. We conduct a systematic benchmark of 17 LLMs using a unified evaluation framework. Our analysis uncovers annotator bias and substantial instability in model judgments. Surprisingly, increased model scale does not guarantee improved annotation quality--smaller, more task-aligned models frequently exhibit more consistent behavior than their larger counterparts. These results highlight important limitations of current LLMs for sensitive annotation tasks in low-resource languages and underscore the need for careful evaluation before deployment.

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