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
Introducing Alfred: an open-source tool for combining foundation models with weak supervision for faster development of academic data sets.
We propose a new strategy for applying large pre-trained language models to novel tasks when labeled training data is limited. Rather than apply the model in a typical zero-shot or few-shot fashion, we treat the model as the basis for labeling functions in a weak supervision framework. To create a classifier, we first prompt the model to answer multiple distinct…
In this webinar, we’ll explain how enterprises can not only accelerate data labeling but iterate, adapt, and improve label accuracy via AI data development.
In this webinar, we’ll provide an overview of LLM distillation, explain how it compares with fine-tuning, and introduce the latest techniques for training SLMs using larger models and knowledge transfer.
Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.
How one large financial institution used call center AI to inform customer experience management with real-time data.
Learn how Snorkel can programmatically help you create massive amounts of high-quality labeled training data in a matter of hours.
This release features new GenAI tools and Multi-Schema Annotation, as well as new enterprise security tools and an updated home page.
RAG is the first step in building LLM-powered AI applications for enterprise use cases.
