Measuring Progress on Scalable Oversight for Large Language Models

TL;DR

This paper explores scalable oversight for large language models by designing empirical experiments where human specialists outperform AI and unaided humans, demonstrating the potential for effective supervision of advanced AI systems.

Contribution

It introduces an experimental framework for studying scalable oversight and provides proof-of-concept results showing human-AI collaboration improves performance on complex tasks.

Findings

Humans with AI outperform AI alone and unaided humans on specific tasks.

Chat-based interactions with language models can enhance human performance.

Scalable oversight research is feasible with current large language models.

Abstract

Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on ways it can be studied empirically. We first present an experimental design centered on tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY. On these tasks, we find that human participants who interact with an unreliable large-language-model dialog assistant through chat -- a trivial baseline strategy for scalable oversight -- substantially outperform both the model alone and their own unaided performance. These results are an encouraging sign that scalable oversight will be tractable to study with present models and bolster recent findings that large language models can productively assist humans with difficult tasks.