OpenAI has developed GPT-4, a large-scale, multimodal model that can process both image and text inputs and generate text outputs. While GPT-4 is not as capable as humans in many real-world scenarios, it performs at a human level on various professional and academic benchmarks, including passing a simulated bar exam with a score in the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. Post-training alignment improves its performance on factuality and adherence to desired behavior. A key part of the project was developing infrastructure and optimization methods that behave predictably across different scales, allowing accurate predictions about GPT-4's performance based on smaller models trained with significantly less compute. GPT-4 outperforms previous large language models and most state-of-the-art systems on traditional NLP benchmarks, including the MMLU benchmark, where it performs well in multiple languages. It also shows strong performance on exams designed for humans, such as the simulated bar exam. GPT-4's capabilities are supported by its pre-training process and are not significantly affected by post-training alignment. However, it has limitations, including not being fully reliable, having a limited context window, and not learning from experience. GPT-4 has significant safety challenges, and careful study of these challenges is important due to the potential societal impact. The report includes an extensive system card discussing risks such as bias, disinformation, over-reliance, privacy, cybersecurity, and proliferation. It also describes interventions to mitigate potential harms, including adversarial testing with domain experts and a model-assisted safety pipeline. The report also discusses the challenge of developing deep learning infrastructure and optimization methods that behave predictably across different scales. This allowed predictions about GPT-4's performance based on smaller models trained with significantly less compute. GPT-4's performance on various benchmarks, including exams and traditional NLP tasks, is detailed, showing its strong capabilities in many areas. Despite its capabilities, GPT-4 has limitations, including the potential for hallucinations and the inability to learn from experience. It is important to use GPT-4's outputs carefully, especially in high-stakes contexts. The report also discusses the risks and mitigations associated with GPT-4, including its potential for generating harmful content and the need for safety measures to prevent such outcomes. GPT-4 has been developed with a focus on safety and alignment, and the report highlights the use of domain experts for adversarial testing and the model-assisted safety pipeline. The report also discusses improvements in safety metrics, including a significant reduction in the model's tendency to respond to requests for disallowed content and an increase in its ability to respond to sensitive requests in accordance with policies. Overall, GPT-4 represents a significant step towards broadly useful and safely deployed AI systems, but there is still much work to be done to ensure its safe andOpenAI has developed GPT-4, a large-scale, multimodal model that can process both image and text inputs and generate text outputs. While GPT-4 is not as capable as humans in many real-world scenarios, it performs at a human level on various professional and academic benchmarks, including passing a simulated bar exam with a score in the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. Post-training alignment improves its performance on factuality and adherence to desired behavior. A key part of the project was developing infrastructure and optimization methods that behave predictably across different scales, allowing accurate predictions about GPT-4's performance based on smaller models trained with significantly less compute. GPT-4 outperforms previous large language models and most state-of-the-art systems on traditional NLP benchmarks, including the MMLU benchmark, where it performs well in multiple languages. It also shows strong performance on exams designed for humans, such as the simulated bar exam. GPT-4's capabilities are supported by its pre-training process and are not significantly affected by post-training alignment. However, it has limitations, including not being fully reliable, having a limited context window, and not learning from experience. GPT-4 has significant safety challenges, and careful study of these challenges is important due to the potential societal impact. The report includes an extensive system card discussing risks such as bias, disinformation, over-reliance, privacy, cybersecurity, and proliferation. It also describes interventions to mitigate potential harms, including adversarial testing with domain experts and a model-assisted safety pipeline. The report also discusses the challenge of developing deep learning infrastructure and optimization methods that behave predictably across different scales. This allowed predictions about GPT-4's performance based on smaller models trained with significantly less compute. GPT-4's performance on various benchmarks, including exams and traditional NLP tasks, is detailed, showing its strong capabilities in many areas. Despite its capabilities, GPT-4 has limitations, including the potential for hallucinations and the inability to learn from experience. It is important to use GPT-4's outputs carefully, especially in high-stakes contexts. The report also discusses the risks and mitigations associated with GPT-4, including its potential for generating harmful content and the need for safety measures to prevent such outcomes. GPT-4 has been developed with a focus on safety and alignment, and the report highlights the use of domain experts for adversarial testing and the model-assisted safety pipeline. The report also discusses improvements in safety metrics, including a significant reduction in the model's tendency to respond to requests for disallowed content and an increase in its ability to respond to sensitive requests in accordance with policies. Overall, GPT-4 represents a significant step towards broadly useful and safely deployed AI systems, but there is still much work to be done to ensure its safe and