Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …

Finetuning language models on a collection of datasets phrased as instructions has been
shown to improve model performance and generalization to unseen tasks. In this paper we …

Evaluating large language model (LLM) based chat assistants is challenging due to their
broad capabilities and the inadequacy of existing benchmarks in measuring human …

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 …

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 …

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 …

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 …

Training large language models (LLMs) with open-domain instruction following data brings
colossal success. However, manually creating such instruction data is very time-consuming …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …