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.

As Snorkel AI prepares to build better enterprise LLM evaluations, we spoke with Yifan Mail from Stanford’s CRFM HELM project.

A customer wanted an llm system for complex contract question answering tasks. We helped them build it—beating the baseline by 64 points.

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.

Snorkel AI helped a client solve the challenge of social media content filtering quickly and sustainably. Here’s how.

Google and Snorkel AI customized PaLM 2 using domain expertise and data development to improve performance by 38 F1 points in a matter of hours.

Microsoft infrastructure facilitates Snorkel AI research experiments, including our recent high rank on the AlpacaEval 2.0 LLM leaderboard.

Humans learn tasks better when taught in a logical order. So do LLMs. Researchers developed a way to exploit this tendency called “Skill-it!”

Fine-tuned representation models are often the most effective way to boost the performance of AI applications. Learn why.

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.

Low-rank adaptation (LoRA) lets data scientists customize GenAI models like LLMs faster than traditional full fine-tuning methods.

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

Snorkel AI CEO Alex Ratner explains his view on the importance of AI in data development and illustrates his position with two case studies.

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.

Enterprises that aim to build valuable GenAI applications must view them from a systems-level. LLMs are just one part of an ecosystem.

Snorkel AI’s Jan. 25 Enterprise LLM Summit focused on one theme: AI data development drives enterprise AI success.

QBE Ventures made a strategic investment in Snorkel AI because it provides what Insurers need: scalable and affordable ways to customize AI.

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 spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG).

New unified prompting UI + RAG features, PDF annotation, Databricks MLflow integration, Snorkel Flow Studio, and datasets load 2x faster!

The Databricks Model Registry integration equips Snorkel Flow users to automatically register custom, use case-specific models.

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

LLMs have claimed the spotlight since the debut of ChatGPT, but BERT models quietly handle most enterprise production NLP tasks.

In its first six months, Snorkel Foundry collaborated on high-value projects with notable companies and produced impressive results.

When done right, advanced classification applications cultivate business value and automation, unlock new business lines, and reduce costs.

A brief guide on how financial institutions could use Google Dialogflow with Snorkel Flow to build better chatbots for retail banking

A proof-of-concept project that combines predictive AI + generative AI to minimize LLM’s risks while keeping their advantages.

Data scientists can fine-tune Llama 2 to adapt it to specific tasks. The Snorkel Flow data development platform makes it easy to do so.