How does Mira Network use Decentralization to cure the "hallucination" problem of large models?

Everyone knows that the biggest obstacle to the implementation of AI large models in vertical application scenarios such as finance, healthcare, and law is that the “illusion” problem in AI output results cannot match the actual application scenarios that require accuracy. How to solve it? Recently, @Mira_Network launched a public testnet with a solution, let me tell you what’s going on:

First, AI large model tools have the phenomenon of “hallucination”, which everyone can perceive, mainly for two reasons:

1. The training data for AI LLMs is not comprehensive enough. Although the data scale is already large, it still cannot cover information from some niche or specialized fields. In this case, AI tends to perform “creative completion,” which can lead to some real-time errors.

2. The essence of AI LLMs fundamentally relies on “probabilistic sampling”. It identifies statistical patterns and correlations in the training data, rather than truly “understanding”. Therefore, the randomness of probabilistic sampling, inconsistencies in training, and inference results can lead to biases in AI when dealing with high-precision factual issues.

How can this problem be solved? A paper has been published on the Cornell University ArXiv platform that verifies the improvement of LLMs result reliability through multiple models.

In simple terms, it means first letting the main model generate results, and then integrating multiple validation models to conduct a “majority voting analysis” on the issue, thereby reducing the “hallucination” generated by the model.

It was found in a series of tests that this method can increase the accuracy of AI output to 95.6%.

In that case, a distributed verification platform is definitely needed to manage and validate the collaborative interaction process between the main model and the verification model. Mira Network is such a middleware network specifically built for validating AI LLMs, creating a reliable verification layer between users and the underlying AI models.

With the existence of this trap verification layer network, integrated services such as privacy protection, accuracy assurance, scalable design, and standardized API interfaces can be realized. By reducing the hallucinations in AI LLMs outputs, it expands the potential for AI to be implemented in various niche application scenarios. This is also a practice of how the 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 that can serve as evidence:

1. After integrating Mira, Gigabrain, a trading platform, can add an additional layer to verify the accuracy of market analysis and predictions, filtering out unreliable suggestions, thus improving the accuracy of AI trading signals and making the application of AI LLMs in DeFi scenarios more reliable.

2. Learnrite utilizes Mira to verify AI-generated standardized exam questions, 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 utilizes Mira’s LLM consensus mechanism to integrate it into the BNB ecosystem, creating a decentralized verification network (DVN), which ensures a certain level of accuracy and security for AI computations executed on the blockchain.

Above.

In fact, Mira Network provides a middleware consensus network service, which is certainly not the only way to enhance AI application capabilities. In reality, options such as enhancing through data-side training, enhancing through multimodal large model interactions, and enhancing through privacy computing with potential cryptographic technologies like ZKP, FHE, and TEE are all available paths. However, compared to others, Mira’s solution is valuable for its quick practical implementation and immediate effectiveness.