From BERT to Qwen: Hate Detection across architectures

Ariadna Mon; Saúl Fenollosa; Jon Lecumberri

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arXiv · 2507.10468

From BERT to Qwen: Hate Detection across architectures

Ariadna Mon·Saúl Fenollosa·Jon Lecumberri

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Abstract

Online platforms struggle to curb hate speech without over-censoring legitimate discourse. Early bidirectional transformer encoders made big strides, but the arrival of ultra-large autoregressive LLMs promises deeper context-awareness. Whether this extra scale actually improves practical hate-speech detection on real-world text remains unverified. Our study puts this question to the test by benchmarking both model families, classic encoders and next-generation LLMs, on curated corpora of online interactions for hate-speech detection (Hate or No Hate).

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Ariadna Mon, Saúl Fenollosa, Jon Lecumberri. 2025-07-14. From BERT to Qwen: Hate Detection across architectures. https://arxiv.org/abs/2507.10468

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