LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models
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arXiv
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| Format: | Preprint |
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2024
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| author | Bouchiha, Mouhamed Amine Telnoff, Quentin Bakkali, Souhail Champagnat, Ronan Rabah, Mourad Coustaty, Mickaël Ghamri-Doudane, Yacine |
| author_facet | Bouchiha, Mouhamed Amine Telnoff, Quentin Bakkali, Souhail Champagnat, Ronan Rabah, Mourad Coustaty, Mickaël Ghamri-Doudane, Yacine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13236 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models Bouchiha, Mouhamed Amine Telnoff, Quentin Bakkali, Souhail Champagnat, Ronan Rabah, Mourad Coustaty, Mickaël Ghamri-Doudane, Yacine Distributed, Parallel, and Cluster Computing Emerging Technologies 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. |
| title | LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models |
| topic | Distributed, Parallel, and Cluster Computing Emerging Technologies |
| url | https://arxiv.org/abs/2404.13236 |