It is well understood from literature that the performance of a machine learning (ML) model is
upper bounded by the quality of the data. While researchers and practitioners have focused …

Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …

Fundamental algorithms such as sorting or hashing are used trillions of times on any given
day. As demand for computation grows, it has become critical for these algorithms to be as …

Large Language Models (LLMs) have seen an impressive wave of advances recently, with
models now excelling in a variety of tasks, such as mathematical reasoning and program …

Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

Program synthesis or code generation aims to generate a program that satisfies a problem
specification. Recent approaches using large-scale pretrained language models (LMs) have …

Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist
programmers or even generate programs themselves could make programming more …

This paper explores the limits of the current generation of large language models for
program synthesis in general purpose programming languages. We evaluate a collection of …

Large pre-trained language models perform remarkably well on tasks that can be done" in
one pass", such as generating realistic text or synthesizing computer programs. However …

Large pre-trained language models such as GPT-3 [10], Codex [11], and Google's language
model [7] are now capable of generating code from natural language specifications of …