--- title: "Pamela Mishkin — author profile and papers" description: "Pamela Mishkin, OpenAI: 2 papers on Elamur Research (2021–2022), h-index 10 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/pamela-mishkin/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/pamela-mishkin/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Pamela Mishkin # Pamela Mishkin Pamela Mishkin's papers here deal with large neural networks trained on web-scale data, in the area of [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/). The work asks how such models can learn broadly useful skills from natural language and how their behaviour can be brought closer to what people actually want. It does this through contrastive pretraining that connects images with text and through fine-tuning steered by human judgements. The most cited of these works is [Learning Transferable Visual Models From Natural Language Supervision](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/) (2021), which introduced CLIP. The paper trains an image encoder and a text encoder together on a large collection of image–caption pairs gathered online, so that matching pairs score higher than mismatched ones. At test time, class names written as text become a classifier that needs no training on the target dataset. Close behind is [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/) (2022). It starts from the observation that scaling up GPT-3 does not by itself stop it from inventing facts or ignoring requests. The authors first fine-tune the model on demonstrations written by human labelers. They then train a reward model on the labelers' rankings of candidate answers and optimise against it with PPO. The later of these papers shifts from multimodal representation learning toward alignment, meaning the use of human preferences to make language models more helpful and less harmful. Mishkin's work here dates from 2021 to 2022 and comes from [OpenAI](https://md.research.elamur.ai/free/en/companies/openai/) in San Francisco. Co-authors include [Sandhini Agarwal](https://md.research.elamur.ai/free/en/authors/sandhini-agarwal/), who appears on both papers, as well as [Alec Radford](https://md.research.elamur.ai/free/en/authors/alec-radford/), [Ilya Sutskever](https://md.research.elamur.ai/free/en/authors/ilya-sutskever/) and [Amanda Askell](https://md.research.elamur.ai/free/en/authors/amanda-askell/). Pamela Mishkin has 2 papers in our library, in 2021–2022, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Sandhini Agarwal](https://md.research.elamur.ai/free/en/authors/sandhini-agarwal/), [Aditya Ramesh](https://md.research.elamur.ai/free/en/authors/aditya-ramesh/), [Alec Radford](https://md.research.elamur.ai/free/en/authors/alec-radford/). 2 papers in our library · 2021–2022 current affiliation [OpenAI](https://md.research.elamur.ai/free/en/companies/openai/) · San Francisco United States [OpenAlex](https://openalex.org/A5028772381) · [**h-index 10**](https://openalex.org/authors/A5028772381) · [18 works](https://openalex.org/works?filter=author.id:A5028772381) · [14,733 citations](https://openalex.org/works?filter=author.id:A5028772381&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [2 works in our library](https://md.research.elamur.ai/free/en/authors/pamela-mishkin/#papers) · [9,069 citations in our library](https://md.research.elamur.ai/free/en/authors/pamela-mishkin/cited-by/) · [⭐ Paper Star rank #123](https://md.research.elamur.ai/free/en/paper-stars/page/2/#star-123) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Pamela+Mishkin) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Pamela+Mishkin) · [dblp](https://dblp.org/search?q=Pamela+Mishkin) · [ACL Anthology](https://aclanthology.org/search/?q=Pamela+Mishkin) ## Affiliations [🏢**OpenAI** San Francisco · United States 2021–2025 · 6 papers*current*](https://md.research.elamur.ai/free/en/companies/openai/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Topics [Language models & pretraining 1](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/) ## Papers The most cited first (OpenAlex and our library). 1. **[Learning Transferable Visual Models From Natural Language Supervision](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)** *(2021 · 8,295 citations)* Most computer vision systems learn a fixed list of categories, so each new concept needs more labeled data. 2. **[Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)** *(2022 · 5,770 citations)* Making language models larger does not by itself make them more helpful: they make up facts, generate toxic text, or disregard the user's request. ## Co-authors here 12 of 19 co-authors with a page, the most papers together first. - [Sandhini Agarwal](https://md.research.elamur.ai/free/en/authors/sandhini-agarwal/) ×2 - [Aditya Ramesh](https://md.research.elamur.ai/free/en/authors/aditya-ramesh/) - [Alec Radford](https://md.research.elamur.ai/free/en/authors/alec-radford/) - [Alex Ray](https://md.research.elamur.ai/free/en/authors/alex-ray/) - [Amanda Askell](https://md.research.elamur.ai/free/en/authors/amanda-askell/) - [Chong Zhang](https://md.research.elamur.ai/free/en/authors/chong-zhang/) - [Chris Hallacy](https://md.research.elamur.ai/free/en/authors/chris-hallacy/) - [Diogo Almeida](https://md.research.elamur.ai/free/en/authors/diogo-almeida/) - [Gabriel Goh](https://md.research.elamur.ai/free/en/authors/gabriel-goh/) - [Girish Sastry](https://md.research.elamur.ai/free/en/authors/girish-sastry/) - [Gretchen Krueger](https://md.research.elamur.ai/free/en/authors/gretchen-krueger/) - [Ilya Sutskever](https://md.research.elamur.ai/free/en/authors/ilya-sutskever/) ## Cited by Papers of our library cite their works 9,069 times (of the 140,884 papers whose bibliographies we read); [574 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/pamela-mishkin/cited-by/).