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

TL;DR

This paper introduces LLMChain, a blockchain-based reputation system that combines automatic and human evaluations to assess and improve trustworthiness of large language models, addressing reliability and ethical concerns.

Contribution

It presents the first blockchain-based decentralized framework for sharing and evaluating LLMs, integrating automatic and human feedback for reputation scoring.

Findings

Effective assessment of seven LLMs demonstrated

Scalable evaluation across two benchmark datasets

Enhanced transparency in LLM trustworthiness

Abstract

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.