7 citations · 16 across the 8 of their papers we have counts for
4 papers · 1 filter
AI Behavioral Science: A Framework and Agenda
Matthew O. Jackson, Qiaozhu Me, Stephanie W. Wang +16
We discuss the challenges and opportunities present in the rapidly emerging area of ``AI Behavioral Science.'' We frame it via three subfields. First, as AI becomes ubiquitous and…
What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
Keyon Vafa, Peter G. Chang, Ashesh Rambachan +1
Foundation models are premised on the idea that sequence prediction can uncover deeper domain understanding, much like how Kepler's predictions of planetary motion later led to the…
Potemkin Understanding in Large Language Models
Marina Mancoridis, Bec Weeks, Keyon Vafa +1
Large language models (LLMs) are regularly evaluated using benchmark datasets. But what justifies making inferences about an LLM's capabilities based on its answers to a curated se…
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…