115 citations · 256 across the 13 of their papers we have counts for
13 papers
"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
Michael Xieyang Liu, Advait Sarkar, Carina Negreanu +4
Code-generating large language models translate natural language into code. However, only a small portion of the infinite space of naturalistic utterances is effective at guiding c…
What is it like to program with artificial intelligence?
Advait Sarkar, Andrew D. Gordon, Carina Negreanu +3
Large language models, such as OpenAI's codex and Deepmind's AlphaCode, can generate code to solve a variety of problems expressed in natural language. This technology has already…
Conditional independence by typing
Maria I. Gorinova, Andrew D. Gordon, Charles Sutton +1
A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. However, this goal is hard to achieve in practice. Users are often forced to r…
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon +1
We present a new approach to the type inference problem for dynamic languages. Our goal is to combine \emph{logical} constraints, that is, deterministic information from a type sys…
Somewhere Around That Number: An Interview Study of How Spreadsheet Users Manage Uncertainty
Judith Borghouts, Andrew D. Gordon, Advait Sarkar +2
Spreadsheet users regularly deal with uncertainty in their data, for example due to errors and estimates. While an insight into data uncertainty can help in making better informed…
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Maria I. Gorinova, Andrew D. Gordon, Charles Sutton
Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifice…