Resource library
Explore our complete library of resources including blogs, benchmarks, research papers, and more.
Grok 4.5 Testing Results: How SpaceXAI’s New Model Performs on Real Professional Work
Why coding agents need better data, evals, and environments
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Evaluating coding agent capabilities with Terminal-Bench: Snorkel’s role in building the next generation benchmark
Building FinQA: An Open RL Environment for Financial Reasoning Agents
The science of rubric design
This release eases Snorkel Flow application creation process and tightens the iteration loop. It also upgrades our security certifications.
NVIDIA’s Nyla Worker presented “Leveraging Synthetic Data to Train Perception Models Using NVIDIA Omniverse Replicator” in 2022.
Google experts Abhishek Ratna and Robert Crowe discuss practical paths to data-centricity in applied AI at The Future of Data-Centric AI ’22.
State Farm senior data scientist Jason Goldfarb presented “Reusable Data Cleaning Pipelines in Python” at the Future of Data-Centric AI 2022.
Jack Zhou, product manager at Arize, on “How to Apply Machine Learning Observability to Your ML System” from The Future of Data-Centric AI
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…
Claypot AI CEO Chip Huyen presented “Platform for Real-Time Machine Learning” at Snorkel AI’s Future of Data-Centric AI 2022.
Enterprises—especially the world’s largest—are excited to use large language models, but they want to fine-tune them on proprietary data.
