Document · Research paper

Program Synthesis with Large Language Models

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Maarten Bosma · Quoc Le

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Published 2021-08-16 authorship evidence authorship evidence

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Program Synthesis with Large Language Models arXiv · published 2021-08-16 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 aa3a7b819906a3ad482970f70b54a486ad8292aec7a95ef0bab72609320ca200). Submission history as arXiv records it (UTC): v1 16 August 2021. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
Program Synthesis with Large Language Models arXiv · published 2021-08-16 · arXiv:2108.07732v1, submitted 16 August 2021 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 820a8de4a9fe8aef947a40d5da13db0d660baf3658b4a9cebf6d30ac5ba351ab). The author list is the byline as printed on the first page.

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In its own wordsThe paper evaluates large language models of 244M to 137B parameters on program synthesis in Python using two benchmarks it introduces, MBPP and MathQA-Python, in few-shot and fine-tuning settings.“We evaluate a collection of such models (with between 244M and 137B parameters) on two new benchmarks, MBPP and MathQA-Python, in both the few-shot and fine-tuning regimes.” arXiv v1, PDF p. 1, abstract2021-08-16 · publication · Stated in source · Supported Inspect claim & evidence ↗