3 papers
cs.HC2026
AI Behavioral Science
Matthew O. Jackson, Qiaozhu Me, Stephanie W. Wang +16
We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it be…
cs.LG2025
What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models
Keyon Vafa, Sarah Bentley, Jon Kleinberg +1
How should we evaluate the quality of generative models? Many existing metrics focus on a model's producibility, i.e. the quality and breadth of outputs it can generate. However, t…
cs.CL2024
Evaluating the World Model Implicit in a Generative Model
Keyon Vafa, Justin Y. Chen, Ashesh Rambachan +2
Recent work suggests that large language models may implicitly learn world models. How should we assess this possibility? We formalize this question for the case where the underlyi…