New framework analyzes LLM bias in content moderation

Researchers have developed a new framework called the Ghost Annotator to analyze human label variation in content moderation tasks, particularly when LLMs are used for annotation. This framework combines conformal prediction with collaborative filtering to model LLM behavior against human annotators, identifying instances where model predictions diverge from human consensus. The study found that larger LLMs tend to be more confident in classifying content that doesn't align with any human annotation, and revealed a consistent pattern of demographic misalignment, suggesting biases in pretraining data. AI

IMPACT This framework could help identify and mitigate biases in LLMs used for content moderation, leading to fairer and more reliable AI systems.

RANK_REASON The cluster contains an academic paper detailing a new framework and methodology for analyzing LLM behavior and bias. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• conformal prediction
• Ghost Annotator
• human annotators
• LLMs
• Marco Antonio Stranisci

• paper
• safety

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