From BERT to Qwen: Hate Detection across architectures
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Abstract (arXiv)
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).
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 14 Jul 2025.
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8 of 17 references.
References · 8
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023Qwen Technical Reportcited by 3,344
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
- 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 417
- 2021Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Modelcited by 26
- 2024MetaHate: A Dataset for Unifying Efforts on Hate Speech Detectioncited by 14
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