Resource library
Evaluating Coding Agent Capabilities with Terminal-Bench: Snorkel’s Role in Building the Next Generation Benchmark
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Benchtalks #1: Alex Shaw (Terminal-Bench, Harbor) – Building the Benchmark Factory
Building FinQA: An Open RL Environment for Financial Reasoning Agents
We need more labeled data than ever, so we have explored weak supervision for non-categorical applications—with notable results.
In this webinar, Vincent Chen, Product Director at Snorkel AI, will discuss the importance of LLM evaluation, highlight common challenges and approaches, explain core concepts such as slices and quality models, and demonstrate Snorkel AI’s approach to LLM evaluation.
To tackle generative AI use cases, Snorkel AI + AWS launched an accelerator program to address the biggest blocker: unstructured data.
AI alignment ensures that AI systems align with human values, ethics, and policies. Here’s a primer on how developers can build safer AI.
The Snorkel Flow label model plays an instrumental role in driving the enterprise value we create. Here’s a peek at how it works.
Learn how Alfred enables users to encode their subject matter expertise via natural language prompts for language and vision-language models.
Vision language models demonstrate impressive image classification capabilities, but LLMs can help improve their performance. Learn how.
Learn how to fine-tune LLMs like Meta’s Llama 3 for specialized tasks and see a live demo of curating high-quality training data with Snorkel Flow
Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.
