Authorship Source Analysis for Large Language Models (LLM) Using a Distributed Ledger
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United States of America
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Apr 10, 2025
app pub date -
Oct 5, 2023
filing date -
Oct 5, 2023
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Abstract
A computing platform may train an LLM by performing a plurality of clustering iterations. The computing platform may record, using a distributed ledger and for each of the plurality of clustering iterations, information of the corresponding clustering iteration. The computing platform may test the LLM, which may reveal at least one error in the LLM. The computing platform may identify, using the distributed ledger, a clustering iteration corresponding to the at least one error and information associated with the clustering iteration. The computing platform may identify, based on the clustering iteration corresponding to the at least one error and the information associated with the clustering iteration, a remediating action. The computing platform may update the LLM by performing the remediating action. The computing platform may retest the LLM. Based on identifying that an accuracy of the LLM meets or exceeds an accuracy threshold, the computing platform may deploy the LLM.
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