How does Mira Network use decentralized networks to cure the stubborn problem of "hallucination" of large models?

Everyone knows that the biggest obstacle to the implementation of large AI models in vertical application scenarios such as finance, medical care, and law is that the "illusion" problem of AI output results cannot match the actual application scenarios that require accuracy. How to solve it? Recently, @Mira_Network launched a public test network and provided a set of solutions. Let me tell you what happened:

First of all, AI big model tools have "hallucinations", which everyone can sense. There are two main reasons:

1. AI LLMs training data is not complete. Although the existing data is large, it still cannot cover some niche or professional fields. At this time, AI tends to do "creative completion" and then cause some real-time errors;

2. AI LLMs essentially rely on "probabilistic sampling", which is to identify statistical patterns and correlations in training data, rather than truly "understanding". Therefore, the randomness of probabilistic sampling, the inconsistency of training and reasoning results, etc. will lead to deviations in AI in dealing with high-precision factual problems;

How to solve this problem? Cornell University published a paper on the ArXiv platform that uses multiple models to jointly verify and improve the reliability of LLMs results.

To put it simply, it means that the main model is first used to generate results, and then multiple verification models are integrated to perform a "majority voting analysis" on the problem, thereby reducing the "hallucinations" created by the model.

In a series of tests, it was found that this method can improve the accuracy of AI output to 95.6%.

In this case, a distributed verification platform is definitely needed to manage and verify the collaborative interaction process between the main model and the verification model. Mira Network is such a middleware network dedicated to building AI LLMs verification, which builds a reliable verification layer between users and basic AI models.

With the existence of this verification layer network, integrated services including privacy protection, accuracy guarantee, scalable design, standardized API interface, etc. can be realized. By reducing the output illusion of AI LLMs, the possibility of AI landing in various application scenarios can be expanded. It is also a practice that the Crypto distributed verification network can play a role in the implementation process of AI LLMs engineering.

For example, Mira Network shared several cases in finance, education, and blockchain ecology to prove:

1) After Gigabrain integrates Mira into a trading platform, the system can add a link to verify the accuracy of market analysis and predictions, filter out unreliable suggestions, improve the accuracy of AI trading signals, and make AI LLMs more reliable in DeFai scenarios;

2) Learnrite uses mira to verify standardized test questions generated by AI, allowing educational institutions to use AI to generate content on a large scale without affecting the accuracy of educational test content to maintain strict educational standards;

3) The blockchain Kernel project utilized Mira’s LLM consensus mechanism and integrated it into the BNB ecosystem, creating a decentralized verification network DVN, which ensures the accuracy and security of AI calculations performed on the blockchain to a certain extent.

above.

In fact, Mira Network provides middleware consensus network services, which is definitely not the only way to enhance AI application capabilities. In fact, data-side training enhancement, multi-modal large model interaction enhancement, and privacy computing enhancement through potential cryptographic technologies such as ZKP, FHE, and TEE are all optional paths. But in comparison, Mira's solution is fast in implementation and has direct effects.