GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives

arXiv:2311.05074v2 Announce Type: replace
Abstract: Human annotation plays a core role in machine learning -- annotations for supervised models, safety guardrails for generative models, and human feedback for reinforcement learning, to cite a few avenues. However, the fact that many of these human annotations are inherently subjective is often overlooked. Recent work has demonstrated that ignoring rater subjectivity (typically resulting in rater disagreement) is problematic within specific tasks and for specific subgroups. Generalizable methods to harness rater disagreement and thus …

abstract analysis annotation annotations arxiv core cs.ai cs.cl feedback framework generative generative models guardrails however human human feedback machine machine learning perspectives reinforcement reinforcement learning replace role safety type