Alignment

Ensure your LLMs align with your values and goals using LLM alignment techniques. Learn how to mitigate risks and optimize performance.

Learn about large language model (LLM) alignment and how it maximizes the effectiveness of AI outputs for organizations.

We aim to help our customers get GenAI into production. In our 2024.R3 release, we’ve delivered some exciting GenAI evaluation results.

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

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.

See a walkthrough of how Snorkel Flow users build applications with production-grade RAG retrieval components.

Snorkel takes a step on the path to enterprise superalignment with new data development workflows for enterprise alignment

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.

Snorkel AI will be at Google Cloud Next. The event will feature more than 700 sessions, so we picked five that we think you shouldn’t miss.

Enterprise GenAI 2024: applications will likely surge toward production, according to Snorkel AI Enterprise LLM Summit survey results .

Training large language models is a multi-layered stack of processes, each with its unique role and contribution to the model’s performance.

LLM distillation isolates task-specific LLM performance and mirrors it in a smaller format—creating faster and cheaper performance.

Snorkel CEO Alex Ratner talks with QBE Ventures’ Alex Taylor about the future of AI, LLMs and multimodal models in the insurance industry.

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

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

Snorkel CEO Alex Ratner chatted with Stanford Professor Percy Liang about evaluation in machine learning and in AI generally.

Insurance claims processing has long required a lot of tedious and expensive human labor, but artificial intelligence (AI) can help.

Ai and ML offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management.

The medical industry is exploding with data. Manually labeling data for clinical trials is a challenge. Fortunately, AI can help.

GPT-3 unlocked additional capacity by automating first drafts of internal updates—including blog summaries and sample tweets.

Snorkel AI teamed up with Hugging Face to provide enterprises with even more flexibility and choice as they develop AI applications.

McKinsey’s Carlo Giovine and David Harvey present “Trends in Enterprise ML and the potential impact of Foundation Models” at Snorkel’s Foundation Model Summit.

Amanpreet Singh, Lead Researcher at Hugging Face gave a presentation entitled Towards Unified Foundation Models for Vision and Language Alignment a Snorkel AI’s Foundation Model Summit in January.

Foundation Models (FMs), such as GPT-3 and Stable Diffusion, mark the beginning of a new era in machine learning and artificial intelligence. What are they and how will they impact your business? Find out in our guide.

Snorkel CEO Alex Ratner interviews Mayee Chen about how Liger improves the effectiveness of programmatic labeling through foundation model embeddings.

Snorkel AI held its Foundation Model Summit Jan 17, bringing together 12 presenters and over 600 attendees at 10 virtual sessions. The event drew registrants from across many sectors, including the tech industry, healthcare, and financial services.

Highlighting the best practices for building and deploying AI models for financial document processing applications AI has massive potential in the financial industry. Building AI models to automate information extraction, fraud detection, and compliance monitoring can provide efficient and faster responses and support repurposing domain experts’ labor to more meaningful tasks. Developing AI models is not just about having models

The following post is based on a talk discussing the benefits of programmatic labeling for trustworthy AI, which was presented as part of the Trustworthy AI: A Practical Roadmap for Government event that took place this past April, with Snorkel AI Co-founder and Head of Technology, Braden Hancock. If you would like to watch Braden’s presentation, we have included it