Integrating human feedback into machine learning frameworks improves model performance. A two-stage framework, "Supervised Fine Tuning+Human Comparison," connects machine learning with human feedback through a probabilistic bisection approach. The LNCA ratio highlights the advantage of incorporating human evaluators in reducing sample complexity. Human comparisons are more effective than estimations due to their ease and precision. The paper provides a theoretical framework for strategic utilization of human comparisons to address noisy data and high-dimensional models.
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arxiv.org
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