GPT-4 reports broader multimodal capability

A large model accepted image and text inputs and generated text outputs.

OpenAI

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The contribution

The report describes GPT-4 evaluation across a range of professional, academic, and language benchmarks, alongside limitations and safety work. It documents a multimodal model while withholding many details of its architecture and training.

What this does not establish

Limited technical disclosure constrains independent reproduction. Strong benchmark results do not establish general intelligence or consistent factual accuracy.

Why this date?

The technical report was first submitted on 15 March 2023. GPT-4o is a separate, later model.

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The original work

GPT-4 Technical Report

Authors: OpenAI
Publication: arXiv:2303.08774
Source type: Technical report

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Cited By

Atlas entries whose reference lists cite this work or the publication version noted below.

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024)
Listed in the source bibliography. Check the reference list.

Citations

Works in the Atlas cited by this entry.

Attention Is All You Need (2017)
Listed in the source bibliography. Check the reference list.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)
Listed in the source bibliography. Check the reference list.
Language Models are Unsupervised Multitask Learners (2019)
Listed in the source bibliography. Check the reference list.
Language Models are Few-Shot Learners (2020)
Listed in the source bibliography. Check the reference list.
Scaling Laws for Neural Language Models (2020)
Listed in the source bibliography. Check the reference list.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)
Listed in the source bibliography. Check the reference list.
Constitutional AI: Harmlessness from AI Feedback (2022)
Listed in the source bibliography. Check the reference list.
Large Language Models are Zero-Shot Reasoners (2022)
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Training language models to follow instructions with human feedback (2022)
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LLaMA: Open and Efficient Foundation Language Models (2023)
Listed in the source bibliography. Check the reference list.

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Reference section 1 (PDF pages 18, 19, 20, 21, 22, 23)

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