LLM Unlearning via Loss Adjustments - Accenture Global Solutions
Summary
USPTO published patent application US20260099772A1 by Accenture Global Solutions Limited disclosing a system and method for large language model unlearning via a forget data only loss adjustment (FLAT) function. The invention involves accessing forget data samples, associating template responses via LLMs, and training a target LLM using loss adjustments to maximize divergence between template and forget answers.
What changed
USPTO published patent application US20260099772A1 titled 'Large Language Model Unlearning via Loss Adjustments' by Accenture Global Solutions Limited on April 9, 2026. The application discloses a method and system for removing learned information from LLMs using a forget data only loss adjustment (FLAT) function, involving accessing forget data samples, associating template responses, and training a target LLM to maximize divergence between available template answers and forget answers.
Technology companies developing or deploying LLMs may find this patent application relevant to their intellectual property portfolios and product development strategies. The disclosed unlearning technique has potential applications in privacy compliance, content moderation, and model governance. However, as this is a published patent application (A1 publication), no enforceable rights or compliance obligations are established at this stage.
What to do next
- Monitor for updates if pursuing similar AI/ML unlearning technology
- Review patent claims for potential licensing implications
Archived snapshot
Apr 10, 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.
LARGE LANGUAGE MODEL UNLEARNING VIA LOSS ADJUSTMENTS
Application US20260099772A1 Kind: A1 Apr 09, 2026
Assignee
ACCENTURE GLOBAL SOLUTIONS LIMITED
Inventors
Jinlong PANG, Jiaheng WEI, Ankit Parag SHAH, Yujia BAO, Yaxuan WANG, Wei WEI, Yang LIU, Quan LIU, Yuhao LIU
Abstract
System and method for LLM unlearning via loss adjustments are disclosed. The method includes accessing forget data samples from one or more datasets, associating a template response for each forget data sample via implementation of one or more LLMs, and training a target LLM using a forget data only loss adjustment (FLAT) function to generate an unlearned LLM, including implementing a loss adjustment to maximize a divergence for between an available template answer and a forget answer only with respect to forget data samples.
CPC Classifications
G06N 20/00
Filing Date
2025-10-01
Application No.
19347258
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