From Specialization to Generalization: Instruction-Tuned LLMs for Robust Harmful Content Mitigation
MCML Authors
Lukas Edman
Dr.
Daryna Dementieva
Dr.
Alexander Fraser
Prof. Dr.
Core PI
Abstract
Lukas Edman
Dr.
Daryna Dementieva
Dr.
Alexander Fraser
Prof. Dr.
Core PI
Abstract
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; Dönmez 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.
misc EDF26
Preprint
Aug. 2026Authors
L. Edman • D. Dementieva • A. FraserLinks
arXiv GitHubResearch Area
BibTeXKey: EDF26