Measuring Progress on Scalable Oversight for Large Language Models \ Anthropic
anthropic.com · 215 words · saved by 1 readers
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Alignment Research Measuring Progress on Scalable Oversight for Large Language Models Nov 4, 2022 Read Paper 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 prese
related reading
- Measuring progress on scalable oversightanthropic.com
- Automated Alignment Researchers: Using large language models to scale scalable oversight \ Anthropicanthropic.com
- Alignment is not solved but it increasingly looks solvablealigned.substack.com
- Oversight Assistants: Turning Compute into Understandingbounded-regret.ghost.io
- Recommendations for Technical AI Safety Research Directionsalignment.anthropic.com
- A statistical approach to model evaluations \ Anthropicanthropic.com
- Core views on AI safety: When, why, what, and how \ Anthropicanthropic.com
- Emergent introspective awareness in large language models \ Anthropicanthropic.com
- Core views on AI safety: When, why, what, and how \ Anthropicanthropic.com
- AI Control: Improving Safety Despite Intentional Subversion — LessWronglesswrong.com
- Auditing language models for hidden objectives \ Anthropicanthropic.com
- Discovering Language Model Behaviors with Model-Written Evaluations — LessWronglesswrong.com