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BlockchainEnabled Large Language Models for Prognostics and Health Management Framework in Industrial Internet of Things Dun LiID1,3, Hongzhi Li(Roberto Minerva 14) IDID12, Jing LiID, Noel Crespi1,
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How to fill out blockchain-enabled large language models

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Identify the purpose and application of the blockchain-enabled large language model.
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Select an appropriate blockchain platform to support the language model's functionalities.
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Gather and preprocess the relevant training data for the language model.
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Implement the architecture of the language model using a suitable programming language and machine learning framework.
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Integrate blockchain technology to ensure security, transparency, and immutability of the data and transactions.
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Train the language model using the prepared data, making adjustments as necessary.
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Test the model for accuracy and efficiency, ensuring it meets the requirements of the intended use.
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Deploy the final model on the blockchain, ensuring proper configuration for scalability and performance.
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Monitor and update the system as needed to improve functionality and address any security concerns.

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Blockchain-enabled large language models are artificial intelligence systems that utilize blockchain technology to enhance their capabilities, improve data integrity, and ensure secure and transparent management of the data used for training and application.
Organizations or individuals developing or deploying blockchain-enabled large language models may be required to file relevant documentation with regulatory bodies, particularly in industries related to data privacy and security.
Filling out blockchain-enabled large language models involves providing the necessary information required by regulatory frameworks, which usually includes technical specifications, intended use, data sources, and security measures.
Reported information typically includes the model architecture, training dataset details, compliance with data protection regulations, and mechanisms used for ensuring data security and integrity.
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