As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …

Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

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 …

As the capabilities of generative language models continue to advance, the implications of
biases ingrained within these models have garnered increasing attention from researchers …

Multimodal datasets are a critical component in recent breakthroughs such as CLIP, Stable
Diffusion and GPT-4, yet their design does not receive the same research attention as model …

In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large
language models~(LLMs), eg, GPT-4, as a referee to score and compare the quality of …

We survey a current, heated debate in the artificial intelligence (AI) research community on
whether large pretrained language models can be said to understand language—and the …

Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

Instruction tuning enables pretrained language models to perform new tasks from inference-
time natural language descriptions. These approaches rely on vast amounts of human …

In the last year alone, a surge of new benchmarks to measure $\textit {compositional} $
understanding of vision-language models have permeated the machine learning ecosystem …