Supporting Human Raters with the Detection of Harmful Content using Large Language Models
<|reference_start|>Supporting Human Raters with the Detection of Harmful Content using Large Language Models: In this paper, we explore the feasibility of leveraging large language models (LLMs) to automate or otherwise assist human raters with identifying harmful content including hate speech, harassment, violent extremism, and election misinformation. Using a dataset of 50,000 comments, we demonstrate that LLMs can achieve 90% accuracy when compared to human verdicts. We explore how to best leverage these capabilities, proposing five design patterns that integrate LLMs with human rating, such as pre-filtering non-violative content, detecting potential errors in human rating, or surfacing critical context to support human rating. We outline how to support all of these design patterns using a single, optimized prompt. Beyond these synthetic experiments, we share how piloting our proposed techniques in a real-world review queue yielded a 41.5% improvement in optimizing available human rater capacity, and a 9--11% increase (absolute) in precision and recall for detecting violative content.<|reference_end|>
@article{thomas2024supporting,
    title={Supporting Human Raters with the Detection of Harmful Content using
  Large Language Models},
    author={Kurt Thomas and Patrick Gage Kelley and David Tao and Sarah Meiklejohn
  and Owen Vallis and Shunwen Tan and Blav{z} Brataniv{c} and Felipe Tiengo
  Ferreira and Vijay Kumar Eranti and Elie Bursztein},
    journal={arXiv preprint arXiv:2406.12800},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12800},
    primaryClass={cs.CR}
}