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Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication

A framework for efficiently and verifiably updating large language model-enabled semantic knowledge bases.

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Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication

By Yijing Lin, Ze Chai, Jiacheng Wang, Zhipeng Gao, Nan Ma, Ping zhang, Dusit NiyatoIEEE Communications Magazine
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The authors propose a proof-of-unlearning framework for updating large language models (LLMs) used in semantic knowledge bases. The framework tracks the evolution of unlearning by measuring drifts in the LoRA adapter subspace. Experimental results demonstrate its effectiveness.

This work addresses the challenge of removing outdated, malicious, or privacy-sensitive content from LLMs without retraining.

Abstract

The authors propose a proof-of-unlearning framework for updating large language models (LLMs) used in semantic knowledge bases. The framework tracks the evolution of unlearning by measuring drifts in the LoRA adapter subspace. Experimental results demonstrate its effectiveness. This work addresses the challenge of removing outdated, malicious, or privacy-sensitive content from LLMs without retraining.

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unlearningllmsemantic knowledge baseproof of unlearningmodel agnostic frameworkLarge Language ModelsSemantic InteroperabilityContent OperationsData Governance
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