Resource library

Even with the rapid advancements to AI made possible by LLMs and Foundation Models, data remains the key to unlocking real value for enterprise AI.

GenAI may be the most transformative technology of the past decade but data is where enterprises are able to realize real value from AI today.

The paper explores the use of pseudolabels, which are heuristic labels for unlabeled data, to enhance the performance of vision-language models like CLIP via prompt tuning. The authors investigate different learning paradigms and prompt modalities and find that iterative prompt-training strategies leveraging CLIP-based pseudolabels lead to significant improvements in CLIP’s image classification performance.

The paper introduces Alfred, a system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. It enables users to encode their subject matter expertise via natural language prompts for language and vision-language models.

The paper proposes a statistical label model called FABLE that incorporates instance features to improve the accuracy of inferred truth in Programmatic Weak Supervision (PWS). FABLE is built on a mixture of Bayesian label models, where the coefficients of the mixture components are predicted by a Gaussian Process classifier based on instance features.

Generative AI is at peak hype and poised to dive into the “trough of despair,” according to the 2023 Gartner® Hype Cycle™ for AI.

We used weak supervision to programmatically curate instruction tuning data for open-source LLMs to build a better GenAI.

Snorkel AI announced a strategic partnership with Together AI to enable organizations to build their own proprietary LLMs on their data.

This release eases Snorkel Flow application creation process and tightens the iteration loop. It also upgrades our security certifications.