When Hate Meets Facts: LLMs-in-the-Loop for Check-worthiness Detection in Hate Speech

arXiv:2603.25269 · cs.CL · Submitted 2026-03-26 · Read on arXiv

cs.CL

Submitted: 2026-03-26

Updated: 2026-08-30

Terminology

• F1 Score — 3× in this paper · explained in 3 episodes
• Annotator Agreement — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Checkworthy Factual Statement — 1× in this paper
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Non-Factual Statement — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Unimportant Factual Statement — 1× in this paper

Sources

• Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
• JudgeLM: Fine-tuned Large Language Models are Scalable Judges
• Measuring AI "Slop" in Text

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