Are LLMs Ready to Replace Bangla Annotators?
#LLMs #Zero‑shot annotators #Bangla #Hate speech #Bias #Low‑resource languages #Automated dataset creation #Human agreement #Identity sensitivity
📌 Key Takeaways
- LLMs are increasingly used as automated tools for dataset creation across languages.
- The reliability and potential bias of LLMs as unbiased annotators remain largely unknown, especially in low‑resource contexts.
- The study evaluates LLMs as zero‑shot annotators for Bangla hate‑speech detection—a task with low human agreement and high risk of bias.
- Human annotators already face challenges in agreeing on Bangla hate‑speech, amplifying concerns about machine bias.
- Bias in annotator output can lead to serious downstream consequences in moderation, policy, and downstream NLP models.
- The research underscores the need to assess LLM performance rigorously before deploying them in sensitive real‑world applications.
📖 Full Retelling
A 2026 study published on arXiv investigates whether large language models (LLMs) can serve reliably as zero‑shot annotators for Bangla hate‑speech detection. The researchers examine how well LLMs perform compared to human annotators in Bangla‑speaking communities, focusing on a task where even humans struggle to reach agreement and where annotator bias carries significant downstream risks. The work addresses an urgent question about the readiness of LLMs to replace human annotators in low‑resource, identity‑sensitive settings.
🏷️ Themes
Large Language Models, Automated Annotation, Bias and Fairness, Low‑Resource Languages, Hate‑Speech Detection, Identity‑Sensitive NLP
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Original Source
arXiv:2602.16241v1 Announce Type: cross
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
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