Data development

We need more labeled data than ever, so we have explored weak supervision for non-categorical applications—with notable results.

Vision language models demonstrate impressive image classification capabilities, but LLMs can help improve their performance. Learn how.

Fine-tuning specialized LLMs demands a lot of time and cost We developed Bonito to make this process faster, cheaper, and easier.

The manufacturing industry has experienced a massive influx of data. Snorkel AI and AWS Sage Maker can make that data actionable.

Snorkel’s Paroma Varma and Google’s Ali Arsenjani discus the role of data in the development and implementation of LLMs.

Snorkel AI placed a model at the top of the AlpacaEval leaderboard. Here’s how we built it, and how it changed AlpacaEval’s metrics.

As Snorkel AI prepares to build better enterprise LLM evaluations, we spoke with Yifan Mail from Stanford’s CRFM HELM project.

Google and Snorkel AI customized PaLM 2 using domain expertise and data development to improve performance by 38 F1 points in a matter of hours.

Humans learn tasks better when taught in a logical order. So do LLMs. Researchers developed a way to exploit this tendency called “Skill-it!”

Fine-tuned representation models are often the most effective way to boost the performance of AI applications. Learn why.

Snorkel AI CEO Alex Ratner explains his view on the importance of AI in data development and illustrates his position with two case studies.

We’ve developed new approaches to scale human preferences and align LLM output to enterprise users’ expectations by magnifying SME impact.

Enterprises that aim to build valuable GenAI applications must view them from a systems-level. LLMs are just one part of an ecosystem.

Snorkel AI’s Jan. 25 Enterprise LLM Summit focused on one theme: AI data development drives enterprise AI success.

Snorkel researchers’ state-of-the-art methods created a 7B LLM that ranked 2nd, behind only GPT-4 Turbo, on AlpacaEval 2.0 leaderboard.