Transparency is often lacking in datasets used to train large language models, study finds
In order to train more powerful large language models, researchers use vast dataset collections that blend diverse data from thousands of web sources. But as these datasets are combined and recombined into multiple collections, important information about their origins and restrictions on how they can be used are often lost or confounded in the shuffle.
AI applications are summarizing articles, writing stories and engaging in long conversations — and large language models are doing the heavy lifting. A large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from…
The performance of artificial intelligence (AI) tools, including large computational models for natural language processing (NLP) and computer vision algorithms, has been rapidly improving over the past decades. One reason for this is that datasets to train these algorithms have exponentially grown, collecting hundreds of thousands of images and texts…
As scientists probe for new insights about DNA, proteins and other building blocks of life, the NVIDIA BioNeMo framework — announced today at NVIDIA GTC — will accelerate their research. NVIDIA BioNeMo is a framework for training and deploying large biomolecular language models at supercomputing scale — helping scientists better…