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Found 6 papers, 1 papers with code
Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models
As language models have scaled both their number of parameters and pretraining dataset sizes, the computational cost for pretraining has become intractable except for the most well-resourced teams.
Data, Data Everywhere: A Guide for Pretraining Dataset Construction
The impressive capabilities of recent language models can be largely attributed to the multi-trillion token pretraining datasets that they are trained on.
Nemotron-4 340B Technical Report
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward.
Nemotron-4 15B Technical Report
We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens.
The Importance of Background Information for Out of Distribution Generalization
Domain generalization in medical image classification is an important problem for trustworthy machine learning to be deployed in healthcare.
Biomedical Information Extraction for Disease Gene Prioritization
We introduce a biomedical information extraction (IE) pipeline that extracts biological relationships from text and demonstrate that its components, such as named entity recognition (NER) and relation extraction (RE), outperform state-of-the-art in BioNLP.
