From BERT to Qwen: Hate Detection across architectures

Ariadna Mon, Saúl Fenollosa, Jon Lecumberri
7/14/2025
cs.CLcs.LG

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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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Cite this paper

@article{mon2025from,
  title  = {From BERT to Qwen: Hate Detection across architectures},
  author = {Ariadna Mon and Saúl Fenollosa and Jon Lecumberri},
  year   = {2025},
  eprint = {2507.10468},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.10468v1}
}

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