7 citations · 16 across the 8 of their papers we have counts for
4 papers · 1 filter
Large Language Models: An Applied Econometric Framework
Jens Ludwig, Sendhil Mullainathan, Ashesh Rambachan
Large language models (LLMs) enable researchers to analyze text at unprecedented scale and minimal cost. Researchers can now revisit old questions and tackle novel ones with rich d…
Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function
Keyon Vafa, Ashesh Rambachan, Sendhil Mullainathan
What makes large language models (LLMs) impressive is also what makes them hard to evaluate: their diversity of uses. To evaluate these models, we must understand the purposes they…
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…