70 citations · 77 across the 5 of their papers we have counts for
5 papers
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'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…
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…
Language Generation in the Limit
Jon Kleinberg, Sendhil Mullainathan
Although current large language models are complex, the most basic specifications of the underlying language generation problem itself are simple to state: given a finite set of tr…
Block Models and Personalized PageRank
Isabel Kloumann, Johan Ugander, Jon Kleinberg
Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these me…