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BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models

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arxiv 2310.06278 v1 pith:276TOJU3 submitted 2023-10-10 cs.NI cs.AIcs.LG

classification cs.NIcs.AIcs.LG
keywords llmsblockchaincontentdifficultlanguagelearningtrustedabove
verification ladder T0 review T1 audit T2 compute T3 formal

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In recent years, artificial intelligence (AI) and machine learning (ML) are reshaping society's production methods and productivity, and also changing the paradigm of scientific research. Among them, the AI language model represented by ChatGPT has made great progress. Such large language models (LLMs) serve people in the form of AI-generated content (AIGC) and are widely used in consulting, healthcare, and education. However, it is difficult to guarantee the authenticity and reliability of AIGC learning data. In addition, there are also hidden dangers of privacy disclosure in distributed AI training. Moreover, the content generated by LLMs is difficult to identify and trace, and it is difficult to cross-platform mutual recognition. The above information security issues in the coming era of AI powered by LLMs will be infinitely amplified and affect everyone's life. Therefore, we consider empowering LLMs using blockchain technology with superior security features to propose a vision for trusted AI. This paper mainly introduces the motivation and technical route of blockchain for LLM (BC4LLM), including reliable learning corpus, secure training process, and identifiable generated content. Meanwhile, this paper also reviews the potential applications and future challenges, especially in the frontier communication networks field, including network resource allocation, dynamic spectrum sharing, and semantic communication. Based on the above work combined and the prospect of blockchain and LLMs, it is expected to help the early realization of trusted AI and provide guidance for the academic community.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration

    cs.CR 2024-12 reject novelty 4.0 of 10

    A hybrid blockchain federated learning framework with Q-learning agents and LoRA-based unlearning is proposed, but the experiments do not show that model utility survives data removal.

  2. From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications

    cs.AI 2025-05 conditional novelty 2.0 of 10

    This paper is a broad tutorial on applying LAMs and agentic AI to 6G, largely restating existing research rather than introducing new results.

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