GPT-4 Technical Report

OpenAI · OpenAI (corporate author)

Published 15 March 2023 · arXiv · Preprint

Summary

This technical report describes GPT-4, a large-scale multimodal Transformer model that accepts image and text inputs and produces text outputs. The report emphasizes that GPT-4 achieves human-level performance on a range of professional and academic benchmarks, and details infrastructure and optimization methods that allowed performance to be predicted from much smaller models. For competitive and safety reasons, the report withholds architecture, dataset, and training details.

Key findings

GPT-4 exhibits human-level performance on numerous professional and academic exams, including scoring in roughly the top 10% on a simulated bar exam.
Predictable scaling enabled accurate forecasting of aspects of GPT-4's performance from models trained with as little as 1/1000th of the compute.
Post-training alignment improved factuality and adherence to desired behavior; an accompanying system card documents safety risks and mitigations.

Subjects & keywords

Artificial Intelligence llm multimodal gpt-4 foundation model

Cite this paper

APA

OpenAI [OpenAI (corporate author)] (2023). GPT-4 Technical Report. arXiv. https://doi.org/10.48550/arXiv.2303.08774

BibTeX

@misc{openai2023gpt4,
  author    = {OpenAI and {OpenAI (corporate author)}},
  title     = {GPT-4 Technical Report},
  journal   = {arXiv},
  year      = {2023},
  doi       = {10.48550/arXiv.2303.08774},
  url       = {https://arxiv.org/abs/2303.08774}
}

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