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arXiv Computation and Language By Lukas Edman, Daryna Dementieva, Alexander Fraser

From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation

Read the original on arXiv Computation and Language →

The paper presents an instruction‑tuned large language model (LLM) based on Qwen3 that is fine‑tuned for hate speech mitigation by unifying 36 English hate speech datasets. The authors show that this generalist LLM achieves state‑of‑the‑art performance on in‑domain benchmarks and delivers significant gains in cross‑domain and cross‑lingual generalization, outperforming specialist encoder‑based classifiers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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