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researcher

Mark Chen

24 papers hereh-index 16134.2k citations82 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author5
  • last author4

Across the 10 of 24 papers where every author was matched, so the position is known.

fields
  • cs.CL13
  • cs.CV6
  • cs.LG5
same name
  • Mark Chen — 14 papers, h 8
  • Mark Chen — 3 papers, h 2
  • Mark Chen — 3 papers, h 1
  • Mark Chen — 2 papers, h 2
  • Mark Chen — 2 papers, h 4
  • Mark Chen — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202025
most citedLanguage Models are Few-Shot Learners

3k citations · 9.5k across the 23 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CV2021★ 1k cited

GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Alex Nichol, Prafulla Dhariwal, Aditya Ramesh +5

Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explor…

cs.LG2021★ 23 cited

Training Verifiers to Solve Math Word Problems

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian +9

State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning. To diagnose the failures…

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.CV2021★ 1.1k cited

Zero-Shot Text-to-Image Generation

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh +5

Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, au…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.