For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

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 …

We present QLoRA, an efficient finetuning approach that reduces memory usage enough to
finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit …

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large
language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Like humans, large language models (LLMs) do not always generate the best output on their
first try. Motivated by how humans refine their written text, we introduce Self-Refine, an …

Because" out-of-the-box" large language models are capable of generating a great deal of
objectionable content, recent work has focused on aligning these models in an attempt to …

Large language models are trained in two stages:(1) unsupervised pretraining from raw text,
to learn general-purpose representations, and (2) large scale instruction tuning and …

Prior work has shown that finetuning large language models (LLMs) using machine-
generated instruction-following data enables such models to achieve remarkable zero-shot …