most citedEvaluating Large Language Models Trained on Code

1.5k citations · 1.7k across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20211.5k cited

Evaluating Large Language Models Trained on Code

Mark Chen, Jerry Tworek, Heewoo Jun +55

We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex p…

cs.SE20212 cited

Safety Case Templates for Autonomous Systems

Robin Bloomfield, Gareth Fletcher, Heidy Khlaaf +2

This report documents safety assurance argument templates to support the deployment and operation of autonomous systems that include machine learning (ML) components. The document…

cs.CY2020219 cited

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Miles Brundage, Shahar Avin, Jasmine Wang +56

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations an…

cs.SE2020

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 2

Robin Bloomfield, Gareth Fletcher, Heidy Khlaaf +8

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS…

cs.SE20202 cited

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1

Robin Bloomfield, Gareth Fletcher, Heidy Khlaaf +8

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS…