GPT-4 Technical Report
OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad + 276 more
TLDR
GPT-4 is a large-scale multimodal Transformer model achieving human-level performance on professional and academic benchmarks through advanced training and alignment techniques.
Key contributions
- Introduces GPT-4, a multimodal model handling both image and text inputs to generate text outputs.
- Demonstrates human-level performance on challenging benchmarks, including scoring in the top 10% on a simulated bar exam.
- Develops scalable infrastructure and optimization methods enabling reliable performance predictions from much smaller models.
Why it matters
This paper is significant because it presents GPT-4, a state-of-the-art AI model that advances the capabilities of language models by integrating multimodal inputs and achieving near-human proficiency on complex tasks. The work also contributes valuable insights into scalable training and alignment strategies, paving the way for more predictable and efficient development of large AI systems.
Original Abstract
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.
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