A Benchmark for Scalable Oversight Protocols

• URL: http://arxiv.org/abs/2504.03731v1
• Date: Mon, 31 Mar 2025 23:32:59 GMT
• Title: A Benchmark for Scalable Oversight Protocols
• Authors: Abhimanyu Pallavi Sudhir, Jackson Kaunismaa, Arjun Panickssery,
• Abstract summary: We introduce a principled framework for evaluating human feedback mechanisms based on our agent score difference (ASD) metric.<n>We supply a Python package to facilitate rapid and competitive evaluation of scalable oversight protocols.
• Score: 2.048226951354646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As AI agents surpass human capabilities, scalable oversight -- the problem of effectively supplying human feedback to potentially superhuman AI models -- becomes increasingly critical to ensure alignment. While numerous scalable oversight protocols have been proposed, they lack a systematic empirical framework to evaluate and compare them. While recent works have tried to empirically study scalable oversight protocols -- particularly Debate -- we argue that the experiments they conduct are not generalizable to other protocols. We introduce the scalable oversight benchmark, a principled framework for evaluating human feedback mechanisms based on our agent score difference (ASD) metric, a measure of how effectively a mechanism advantages truth-telling over deception. We supply a Python package to facilitate rapid and competitive evaluation of scalable oversight protocols on our benchmark, and conduct a demonstrative experiment benchmarking Debate.

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