--- title: "Prafulla Dhariwal — author profile and papers" description: "Prafulla Dhariwal: 3 papers on Elamur Research (2020–2022), h-index 15 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/prafulla-dhariwal/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/prafulla-dhariwal/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Prafulla Dhariwal # Prafulla Dhariwal Prafulla Dhariwal works on large generative models in machine learning. Their papers here span [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and [vision and language](https://md.research.elamur.ai/free/en/topics/multimodal/). A recurring question is how autoregressive Transformers and related generative systems behave as model size and compute grow, and how such models can produce text or images from a prompt. The work is empirical: it trains families of models and evaluates them across many benchmarks and data domains. The most cited of these papers is [Language Models are Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/) (2020). It introduced GPT-3 and showed that a very large autoregressive language model can take on new tasks from instructions and a handful of worked examples placed in its prompt, with no further training. The authors named this ability in-context learning. A related study, [Scaling Laws for Autoregressive Generative Modeling](https://md.research.elamur.ai/free/en/papers/henighan-2020-scaling-laws-autoregressive-generative-modeling/) (2020), asked whether the smooth relationship between loss, model size and compute seen for text also holds elsewhere. It found that the relationship holds for images, video, image–caption pairs and synthetic mathematics problems alike. The newest work turns toward image synthesis. [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://md.research.elamur.ai/free/en/papers/ramesh-2022-hierarchical-text-conditional-image-generation/) (2022) describes unCLIP. In this two-stage approach, a prior first predicts a CLIP image embedding from a caption, and a diffusion decoder then renders that embedding as a picture. Further diffusion stages raise the image to high resolution. The study found that a diffusion-based prior was both cheaper and better than an autoregressive one. Dhariwal's papers from 2021 to 2022 list [OpenAI](https://md.research.elamur.ai/free/en/companies/openai/) as the affiliation. Collaborators include [Tom B. Brown](https://md.research.elamur.ai/free/en/authors/tom-b-brown/), [Jared Kaplan](https://md.research.elamur.ai/free/en/authors/jared-kaplan/), Benjamin Mann, Mark Chen and [Aditya Ramesh](https://md.research.elamur.ai/free/en/authors/aditya-ramesh/). Prafulla Dhariwal has 3 papers in our library, in 2020–2022, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/); often with [Benjamin Mann](https://md.research.elamur.ai/free/en/authors/benjamin-mann/), [Jared Kaplan](https://md.research.elamur.ai/free/en/authors/jared-kaplan/), [Mark Chen](https://md.research.elamur.ai/free/en/authors/mark-chen/). 3 papers in our library · 2020–2022 [OpenAlex](https://openalex.org/A5076418776) · [**h-index 15**](https://openalex.org/authors/A5076418776) · [17 works](https://openalex.org/works?filter=author.id:A5076418776) · [10,006 citations](https://openalex.org/works?filter=author.id:A5076418776&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/prafulla-dhariwal/#papers) · [🔗 13,378 citations in our library](https://md.research.elamur.ai/free/en/authors/prafulla-dhariwal/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Star rank #126](https://md.research.elamur.ai/free/en/paper-stars/page/2/#star-126) 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=Prafulla+Dhariwal) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Prafulla+Dhariwal) · [dblp](https://dblp.org/search?q=Prafulla+Dhariwal) · [ACL Anthology](https://aclanthology.org/search/?q=Prafulla+Dhariwal) ## Affiliations [🏢**OpenAI** San Francisco · United States 2021–2022 · 3 papers](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. 3 more institutions on single bylines are not listed. ## Topics [Language models & pretraining (1)](https://md.research.elamur.ai/free/en/topics/language-models/) · [Vision & language (1)](https://md.research.elamur.ai/free/en/topics/multimodal/) ## Works by year ### 2020s [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/ramesh-2022-hierarchical-text-conditional-image-generation/) · [**2020** 2 papers](https://md.research.elamur.ai/free/en/authors/prafulla-dhariwal/year/2020/) ## Papers The most cited first (OpenAlex and our library). 1. **[Language Models are Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)** *(2020 · 10,782 citations)* The standard approach in NLP has a significant drawback: after pre-training, a model is usually fine-tuned on thousands of labeled examples for each individual task. 2. **[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://md.research.elamur.ai/free/en/papers/ramesh-2022-hierarchical-text-conditional-image-generation/)** *(2022 · 2,599 citations)* The paper addresses text-to-image synthesis by building on CLIP's shared image–text representation space. The OpenAI authors propose unCLIP, a two-stage system. A prior maps a caption to a plausible CLIP image embedding, and a diffusion decoder turns that embedding into a picture. Two diffusion upsamplers then raise… 3. **[Scaling Laws for Autoregressive Generative Modeling](https://md.research.elamur.ai/free/en/papers/henighan-2020-scaling-laws-autoregressive-generative-modeling/)** *(2020 · 115 citations)* The authors from OpenAI examine whether the scaling laws previously found for language models also hold for other data types. ## Also in our library 1 more work of theirs is in our library without a page here yet; it will appear as we publish them. ## Co-authors here 12 of 16 co-authors with a page, the most papers together first. - [Benjamin Mann](https://md.research.elamur.ai/free/en/authors/benjamin-mann/) ×2 - [Jared Kaplan](https://md.research.elamur.ai/free/en/authors/jared-kaplan/) ×2 - [Mark Chen](https://md.research.elamur.ai/free/en/authors/mark-chen/) ×2 - [Tom B. Brown](https://md.research.elamur.ai/free/en/authors/tom-b-brown/) ×2 - [Aditya Ramesh](https://md.research.elamur.ai/free/en/authors/aditya-ramesh/) - [Amanda Askell](https://md.research.elamur.ai/free/en/authors/amanda-askell/) - [Ariel Herbert-Voss](https://md.research.elamur.ai/free/en/authors/ariel-herbert-voss/) - [Arvind Neelakantan](https://md.research.elamur.ai/free/en/authors/arvind-neelakantan/) - [Chris Hallacy](https://md.research.elamur.ai/free/en/authors/chris-hallacy/) - [Christopher Hesse](https://md.research.elamur.ai/free/en/authors/christopher-hesse/) - [Girish Sastry](https://md.research.elamur.ai/free/en/authors/girish-sastry/) - [Heewoo Jun](https://md.research.elamur.ai/free/en/authors/heewoo-jun/) ## Cited by Papers of our library cite their works 13,378 times (of the 140,884 papers whose bibliographies we read); [891 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/prafulla-dhariwal/cited-by/).