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.

Making language models bigger does not inherently make them better at following a user's intent. We show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback.

We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively.

We explore how generating a chain of thought—a series of intermediate reasoning steps—significantly improves the ability of large language models to perform complex reasoning.

We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.

Large pre-trained language models have been shown to store factual knowledge in their parameters. However, their ability to access and precisely manipulate knowledge is still limited. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation.

As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs.

We contend that GPT-4 is part of a new cohort of LLMs that exhibit more general intelligence than previous AI models. We demonstrate GPT-4's capabilities across various domains including mathematics, coding, vision, medicine, law, and psychology.