# Caleb Kaplan

Caleb Kaplan is a computer scientist known for co-authoring the GPT-3 paper, which introduced a large-scale language model with 175 billion parameters, and for his work on scaling laws in artificial intelligence.

Caleb Kaplan is a computer scientist and researcher in the field of artificial intelligence. He is best known for his co-authorship of the paper introducing GPT-3, a large language model developed by OpenAI, and for his contributions to understanding scaling laws in neural networks. His work has been influential in the development of modern generative AI systems.

Kaplan's research focuses on the empirical behavior of large-scale machine learning models, particularly how performance improves with increased model size, data, and compute. His findings have shaped how AI laboratories allocate resources for training large models.

## Early Career and Education

Kaplan completed his undergraduate studies in computer science, though specific dates and institutions are not publicly documented. He later joined OpenAI, where he became part of the research team working on language models. His early work involved analyzing the training dynamics of transformers and other neural network architectures.

## GPT-3 and Scaling Laws

In 2020, Kaplan co-authored the paper "Language Models are Few-Shot Learners," which introduced GPT-3, a model with 175 billion parameters. This work demonstrated that scaling up model size dramatically improved performance on a wide range of natural language tasks without task-specific fine-tuning. The paper became a landmark in the field of deep learning.

Prior to that, in 2020, Kaplan also contributed to the study "Scaling Laws for Neural Language Models," which proposed that model performance follows a power-law relationship with compute, dataset size, and parameter count. These scaling laws provided a predictive framework for training large models efficiently.

## Contributions to AI Research

Kaplan's research has been cited extensively in the AI community. His work on scaling laws has informed the design of subsequent large language models, including those developed by other organizations. He has also explored topics such as model interpretability and the ethical implications of AI deployment.

At OpenAI, Kaplan collaborated with researchers including [jakob-uszkoreit](https://www.wikiprompt.org/wiki/jakob-uszkoreit) and [lukasz-kaiser](https://www.wikiprompt.org/wiki/lukasz-kaiser), who were also involved in transformer architecture development. His insights into training stability and optimization have been incorporated into practical training pipelines.

## Later Work and Industry Impact

After his time at OpenAI, Kaplan's career trajectory is less publicly documented. He has not been associated with major AI companies like [anthropic](https://www.wikiprompt.org/wiki/anthropic) or [google-deepmind](https://www.wikiprompt.org/wiki/google-deepmind) as of 2025. His academic contributions remain a reference point for researchers studying model scaling and efficiency.

Kaplan's work has indirectly influenced hardware design, as companies like [nvidia](https://www.wikiprompt.org/wiki/nvidia) and [amd](https://www.wikiprompt.org/wiki/amd) have optimized chips for large-scale training workloads. However, he has not held formal advisory roles at these firms.

## Legacy and Recognition

The GPT-3 paper has been cited thousands of times and is considered a foundational work in generative AI. Kaplan's co-authorship places him among a group of researchers who advanced the practical capabilities of [large-language-model](https://www.wikiprompt.org/wiki/large-language-model)s. His scaling law analysis remains a standard tool for estimating compute requirements in AI research.

Kaplan's contributions are often discussed alongside those of [david-kaplan](https://www.wikiprompt.org/wiki/david-kaplan), another researcher in the field, though they are distinct individuals. His work continues to be taught in courses on machine learning and AI ethics.

## See Also

- [openai](https://www.wikiprompt.org/wiki/openai)
- [transformer](https://www.wikiprompt.org/wiki/transformer)
- [generative-ai](https://www.wikiprompt.org/wiki/generative-ai)
- [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence)

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Source: https://www.wikiprompt.org/wiki/caleb-kaplan
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-12T16:20:49.42553+00:00