# LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models

arXiv: https://arxiv.org/abs/2404.13236 · HTML: https://mlsanity.com/paper/2404.13236/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2024.

## Built on by (most cited first, 6 of 6)

- [Blockchain for Large Language Model Security and Safety: A Holistic Survey](https://mlsanity.com/paper/2407.20181/) (2024)
- [Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning](https://mlsanity.com/paper/2406.04076/) (2024)
- [Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics](https://mlsanity.com/paper/2509.12233/) (2025)
- [TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction](https://mlsanity.com/paper/2607.05001/) (2026)
- [Robust Reputation-Driven Crowdsourced Federated Learning](https://mlsanity.com/paper/2608.08574/) (2026)
- [DART: A DAG-Based Reputation and Incentive Framework via Blockchain-Enabled Governance for Trustworthy LLM Multi-Agent Collaboration](https://mlsanity.com/paper/2609.05529/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors undermine trust in LLMs and pose significant hurdles to their adoption in real-world applications, such as legal assistance and medical diagnosis, where precision, reliability, and ethical considerations are paramount. These could also lead to user dissatisfaction, which is currently inadequately assessed and captured. Therefore, to effectively and transparently assess users' satisfaction and trust in their interactions with LLMs, we design and develop LLMChain, a decentralized blockchain-based reputation system that combines automatic evaluation with human feedback to assign contextual reputation scores that accurately reflect LLM's behavior. LLMChain not only helps users and entities identify the most trustworthy LLM for their specific needs, but also provides LLM developers with valuable information to refine and improve their models. To our knowledge, this is the first time that a blockchain-based distributed framework for sharing and evaluating LLMs has been introduced. Implemented using emerging tools, LLMChain is evaluated across two benchmark datasets, showcasing its effectiveness and scalability in assessing seven different LLMs.