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This study investigates the effectiveness of instruction-tuned large language models (LLMs) in the domain of hate speech detection, an area where traditional models have shown limited success. By unifying 36 diverse English hate speech datasets and fine-tuning a generalist LLM based on Qwen3, the authors achieve state-of-the-art performance not only in in-domain benchmarks but also in cross-domain and cross-lingual contexts. These findings indicate that instruction tuning can significantly enhance the robustness of LLMs in sensitive applications, outperforming specialized encoder-based models.
Instruction-tuned LLMs can outperform specialized models in hate speech detection, achieving state-of-the-art results across multiple domains and languages.
Large language models (LLMs) demonstrate impressive performance across a wide range of general NLP tasks; however, their effectiveness in sensitive domains, such as hate speech detection, remains less clear. Prior studies comparing prompted LLMs with state-of-the-art encoder-based models (e.g., BERT variants (Roy et al., 2023; D\"onmez et al., 2024)) have shown only marginal gains, suggesting that LLMs may not excel in hate speech detection or mitigation. In this work, we revisit this question through the lens of instruction tuning. By thoroughly unifying 36 English hate speech datasets spanning multiple labeling schemes, we fine-tune a generalist LLM, based on Qwen3 (Qwen Team, 2025), specifically for hate speech mitigation. Our results demonstrate not only state-of-the-art performance on in-domain benchmarks but also substantial improvements in cross-domain and cross-lingual generalization--areas where encoder-based specialist classifiers often struggle.
