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Browse research blogs and academic papers
Snorkel and affiliated academic labs have been hard at work reducing how computationally expensive large language models are.
Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation…
Enterprises—especially the world’s largest—are excited to use large language models, but they want to fine-tune them on proprietary data.
Peter Mattson, Google senior staff engineer and president of MLCommons.org, explained MLCommons at The Future of Data-Centric AI in 2022.
Large language models have enormous potential. But what are they? Where did they come from? And how can you make them work better?
Stanford assistant professor James Zou, presents “Responsible Data-Centric AI for Healthcare and Medicine” at The Future of Data-Centric AI.
Snorkel AI has accepted the first batch of applications for its first annual virtual poster competition. But there’s still time to add yours to the mix.
Join us on June 7-8 to learn how to use your data to build your AI moat at The Future of Data-Centric AI 2023 free virtual conference.
Sharon Li is an assistant professor at the University of Wisconsin-Madison. She presented “Detecting Data Distributional Shift: Challenges and Opportunities” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. The talk covered a novel approach for handling out-of-distribution objects.
