Do We Still Need Humans in the Loop? Human vs. LLM Annotation in Active Learning for TikTok Hate Speech Detection
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arXiv:2604. 13899v3 Announce Type: replace-cross Abstract: Instruction-tuned LLMs can annotate thousands of instances at low cost.
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.
arXiv:2511. 14117v2 Announce Type: replace Abstract: Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotators into a majority vote.
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...
arXiv:2606. 08718v1 Announce Type: cross Abstract: While Deep Active Learning (DAL) effectively reduces human annotation costs, its efficacy is constrained by human annotation errors.
arXiv:2606. 28772v1 Announce Type: cross Abstract: Hate speech annotation pipelines routinely collapse annotator disagreement into majority vote labels before training.