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High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Anthropic
An Anthropic research paper (Nov 2022) providing early empirical evidence that scalable oversight is tractable with present LLMs; foundational for researchers working on human-AI collaboration, debate, and amplification approaches to alignment.
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Importance: 72/100blog postprimary source
Summary
This paper proposes an experimental framework for empirically studying scalable oversight—the challenge of supervising AI systems that may surpass human abilities. Using MMLU and QuALITY benchmarks, the authors demonstrate that humans assisted by an unreliable LLM dialog assistant substantially outperform both the model alone and unaided humans, suggesting scalable oversight is empirically tractable with current models.
Key Points
- •Scalable oversight requires supervising AI systems that may outperform humans on relevant tasks; this paper proposes an empirical framework to study it with current models.
- •Experimental design focuses on tasks where human specialists succeed but unaided humans and general AI systems fail, enabling controlled study of human-AI oversight dynamics.
- •Human participants using an unreliable LLM assistant via chat substantially outperformed both unaided humans and the model alone on MMLU and QuALITY tasks.
- •Results serve as a proof-of-concept that even trivial human-AI interaction strategies can enhance oversight performance, making the problem empirically approachable today.
- •Findings bolster confidence that LLMs can productively assist humans on difficult tasks, with implications for alignment research on debate and amplification approaches.
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| AI Safety via Debate | Approach | 70.0 |
| Scalable Oversight | Research Area | 68.0 |
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AlignmentResearch # Measuring Progress on Scalable Oversight for Large Language Models Nov 4, 2022 [Read Paper](https://arxiv.org/abs/2211.03540) ## 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. [Share on Twitter](https://twitter.com/intent/tweet?text=https://www.anthropic.com/research/measuring-progress-on-scalable-oversight-for-large-language-models)[Share on LinkedIn](https://www.linkedin.com/shareArticle?mini=true&url=https://www.anthropic.com/research/measuring-progress-on-scalable-oversight-for-large-language-models) ## Related content ### Labor market impacts of AI: A new measure and early evidence [Read more](https://www.anthropic.com/research/labor-market-impacts) ### An update on our model deprecation commitments for Claude Opus 3 [Read more](https://www.anthropic.com/research/deprecation-updates-opus-3) ### The persona selection model [Read more](https://www.anthropic.com/research/persona-selection-model) Measuring Progress on Scalable Oversight for Large Language Models \ Anthropic
Resource ID:
72d83671b5f929a1 | Stable ID: MjM2MWUzMm