activity
20192022
most citedEvaluating Large Language Models Trained on Code

1.5k citations · 2.3k across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2022★ 9 cited

DIANES: A DEI Audit Toolkit for News Sources

Xiaoxiao Shang, Zhiyuan Peng, Qiming Yuan +4

Professional news media organizations have always touted the importance that they give to multiple perspectives. However, in practice the traditional approach to all-sides has favo…

cs.CL2022★ 152 cited

Text and Code Embeddings by Contrastive Pre-Training

Arvind Neelakantan, Tao Xu, Raul Puri +22

Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use c…

cs.LG2021★ 1.5k 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.LG2021★ 21 cited

Asymmetric self-play for automatic goal discovery in robotic manipulation

OpenAI OpenAI, Matthias Plappert, Raul Sampedro +13

We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. We rely on asymmetric self-play…

cs.LG2019★ 635 cited

Solving Rubik's Cube with a Robot Hand

OpenAI, Ilge Akkaya, Marcin Andrychowicz +16

We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key comp…