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
Large language models open many new opportunities for data science teams, but enterprise LLM challenges persist—and customization is key.
Objective: We sought to develop a weak supervision-based approach to demonstrate feasibility of post-market surveillance of wearable devices that render AF pre-diagnosis. Materials and Methods: Two approaches were evaluated to reduce clinical note labeling overhead for creating a training set for a classifier: one using programmatic codes, and the other using prompts to large language models (LLMs). Probabilistically labeled notes…
A promising approach for improving the performance of vision-language models like CLIP for image classification is to extend the class descriptions (i.e., prompts) with related attributes, e.g., using brown sparrow instead of sparrow. However, current zero-shot methods select a subset of attributes regardless of commonalities between the target classes, potentially providing no useful information that would have helped to distinguish…
LLM distillation will become a more important in 2024, according to a poll of attendees at Snorkel AI’s 2023 Enterprise LLM virtual summit.
Introduction: With the advent of consumer-facing devices that can render atrial fibrillation (AF) pre-diagnosis, medical wearables now have the potential to affect diagnosis rates and medical care. Post-market surveillance is necessary to understand the impact of wearables on patient outcomes and health care utilization, but is hindered by the lack of codified terms in EHR that capture wearable use. Research…
LLMs have a broad but shallow knowledge, but fall short on specialized tasks. For best performance, enterprises must fine tune their LLMs.
The 2023.R3 Snorkel Flow release is packed with improvements that amplify user experience, streamline workflows, and enhance performance, ensuring our users derive unparalleled value from our platform.
The Biden administration issued an executive order that creates new AI standards and challenges. AI data development can help.
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…
