Mira Network is a middleware network specifically designed to validate AI LLMs, creating a reliable verification layer between users and underlying AI models.
Written by: Haotian
Everyone knows that the biggest obstacle to the application of large AI models in vertical fields such as finance, healthcare, and law is the "hallucination" problem of AI output results, which cannot match the precision required in real-world applications. How to solve this? Recently, @Mira_Network launched a public testnet, providing a solution. Let me explain what’s going on:
First, the occurrence of "hallucinations" in AI large model tools is something everyone can perceive, mainly for two reasons:
The training data for AI LLMs is not complete enough. Although the data scale is already very large, it still cannot cover information from niche or specialized fields. At this point, AI tends to make "creative completions," leading to some real-time errors;
The work of AI LLMs essentially relies on "probability sampling." It identifies statistical patterns and correlations in the training data rather than truly "understanding." Therefore, the randomness of probability sampling and inconsistencies in training and inference results can lead to deviations when AI handles high-precision factual questions;
How to solve this problem? A method to improve the reliability of LLMs results through joint verification by multiple models was published on the Cornell University ArXiv platform.
Simply put, the main model first generates results, and then multiple verification models are integrated to conduct a "majority vote analysis" on the issue, thereby reducing the "hallucinations" produced by the model.
In a series of tests, it was found that this method can increase the accuracy of AI output to 95.6%.
Given this, a distributed verification platform is certainly needed to manage and verify the collaborative interaction process between the main model and the verification models. Mira Network is such a middleware network specifically built for validating AI LLMs, creating a reliable verification layer between users and underlying AI models.
With the existence of this verification layer network, integrated services such as privacy protection, accuracy assurance, scalable design, and standardized API interfaces can be realized. By reducing the hallucinations of AI LLMs output, it expands the possibilities for AI to be implemented in various niche application scenarios. This is also a practical application of how a Crypto distributed verification network can play a role in the engineering implementation process of AI LLMs.
For example, Mira Network shared several cases in finance, education, and the blockchain ecosystem to support this:
1) After integrating Mira, Gigabrain, a trading platform, can add a verification layer to enhance the accuracy of market analysis and predictions, filtering out unreliable suggestions, thus improving the accuracy of AI trading signals and making AI LLMs more reliable in DeFi scenarios;
2) Learnrite utilizes Mira to verify standardized exam questions generated by AI, allowing educational institutions to leverage AI-generated content on a large scale while maintaining the accuracy of educational testing content to uphold strict educational standards;
3) The blockchain Kernel project integrated Mira's LLM consensus mechanism into the BNB ecosystem, creating a decentralized verification network (DVN), which ensures a certain degree of accuracy and security for AI computations executed on the blockchain.
That’s all.
In fact, Mira Network provides middleware consensus network services, which is certainly not the only way to enhance AI application capabilities. In fact, enhancing through training on the data side, enhancing through interactions of multimodal large models, and enhancing through potential cryptographic technologies such as ZKP, FHE, TEE for privacy computing are all optional paths. However, compared to these, Mira's solution is valuable for its quick practical implementation and immediate effectiveness.
免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。
