Category
Snorkel AI emerged from a research project, and we remain closely connected to the research community. Students and professors associated with the Snorkel project continue to publish academic papers that push the field forward, and the Snorkel AI research team integrates the most promising of those ideas into our platform.
Our picks
Getting better performance from foundation models (with less data)
Getting better performance from foundation models (with less data)
August 4, 2023
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Snorkel AI researchers present 18 papers at NeurIPS 2023
The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models. Snorkel AI is proud of its roots in the research community and endeavors to remain at the forefront
October 31, 2023
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All articles on Research
New benchmark results demonstrate value of Snorkel AI approach to LLM alignment
Snorkel researchers’ state-of-the-art methods created a 7B LLM that ranked 2nd, behind only GPT-4 Turbo, on AlpacaEval 2.0 leaderboard.
January 24, 2024
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Retrieval augmented generation (RAG): a conversation with its creator
Snorkel CEO Alex Ratner spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG).
January 16, 2024
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Stanford professor discusses exciting advances in foundation model evaluation
Snorkel CEO Alex Ratner chatted with Stanford Professor Percy Liang about evaluation in machine learning and in AI generally.
January 2, 2024
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Snorkel AI researchers present 18 papers at NeurIPS 2023
The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models. Snorkel AI is proud of its roots in the research community and endeavors to remain at the forefront
October 31, 2023
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Two approaches to distill LLMs for better enterprise value
Distillation techniques allow enterprises to access the full predictive power of large language models at a tiny fraction of their cost.
October 31, 2023
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Bloomberg’s Gideon Mann on the power of domain specialist LLMs
Gideon Mann, head of ML Product and Research at Bloomberg LP, chatted with Snorkel CEO Alex Ratner about building BloombergGPT.
October 17, 2023
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Which is better, retrieval augmentation (RAG) or fine-tuning? Both.
Professionals in the data science space often debate whether RAG or fine-tuning yields the better result. The answer is “both.”
September 20, 2023
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Former U.S. Chief Data Scientist on past and future of data science
Past U.S. Chief Data Scientist DJ Patil talked with Snorkel AI CEO Alex Ratner on topics including the origin of the title “data scientist.”
September 12, 2023
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4 new papers show foundation models can build on themselves
The surest way to improve foundation models is through more and better data, but Snorkel researchers showed FMs can learn from themselves.
August 31, 2023
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Accelerating predictive task time to value with generative AI
Generative AI can write poems, recite common knowledge, and extract information. GenAI can also help quickly build predictive pipelines.
August 17, 2023
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Getting better performance from foundation models (with less data)
Getting better performance from foundation models (with less data)
August 4, 2023
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Data fuels enterprise AI value: 6 takeaways from the Gartner Hype Cycle for Artificial Intelligence, 2023
GenAI may be the most transformative technology of the past decade but data is where enterprises are able to realize real value from AI today.
August 2, 2023
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How we built better GenAI with programmatic data development
We used weak supervision to programmatically curate instruction tuning data for open-source LLMs to build a better GenAI.
July 19, 2023
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