SAFETY ALIGNMENT FOR LANGUAGE MODELS USING MODEL-GENERATED SAFETY CATEGORIES
Summary
USPTO published patent application US20260099707A1 for safety alignment techniques in language models. The application describes using an ensemble of generative AI models to generate machine-defined safety labels for interactions, applying majority voting with predefined safety labels to revise training data labels, and training language models to implement guardrails restricting unsafe content generation. The application covers ensemble-based safety labeling and alignment training methodologies for AI systems.
What changed
USPTO published a patent application covering techniques for aligning language models with safety guardrails. The disclosed methods involve using an ensemble of generative AI models to generate machine-defined safety labels for interactions, revising training data labels through majority voting between machine-defined and predefined safety labels, and training language models to restrict unsafe content outputs. The techniques address how AI systems can be trained to recognize and filter potentially unsafe responses.
Technology companies developing generative AI language models should monitor this application for potential implications on AI safety and alignment practices. While patent applications do not automatically create licensing obligations, if granted, the patent could affect how companies implement model safety training techniques.
What to do next
- Monitor for patent grant and potential licensing implications
- Review intellectual property strategy for AI safety techniques
Archived snapshot
Apr 15, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
SAFETY ALIGNMENT FOR LANGUAGE MODELS BASED ON LANGUAGE MODEL-GENERATED SAFETY CATEGORIES
Application US20260099707A1 Kind: A1 Apr 09, 2026
Inventors
Shaona GHOSH, Prasoon Varshney, Makesn Narsimhan Sreedhar, Aishwarya Padmakumar, Traian Eugen Rebedea, Christopher Marc Rarisien
Abstract
In various examples, techniques for training a language model to implement guardrails on generated outputs include receiving a data set including a plurality of interactions with a language model, each interaction of the plurality of interactions being associated with a predefined safety label; generating, using an ensemble of generative artificial intelligence models, one or more machine-defined safety labels for each interaction in the plurality of interactions; generating a training data set based on revising a label associated with each interaction of the plurality of interactions, the revising being based on a majority vote of the one or more machine-defined safety labels and the predefined safety label associated with each interaction of the plurality of interactions; and training the language model based on the training data set, wherein the training implements guardrails on an output of the language model such that the language model is restricted from generating responses including unsafe content.
CPC Classifications
G06N 3/08 G06N 20/20
Filing Date
2025-09-17
Application No.
19331849
Named provisions
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