Technical Interpretation of Mira: How to Solve the "Hallucination" Problem of AI Large Models

Author: Haotian

Everyone knows that the biggest obstacle for AI large models to be implemented in vertical application scenarios 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 it? Recently, @Mira_Network launched a public testnet and provided a set of solutions. Let me explain 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 complete enough. Although the scale of existing data is very large, it still cannot cover information in some niche or specialized fields. At this time, AI tends to make “creative completions,” which can lead to some real-time errors.

2. The essence of AI LLMs fundamentally 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 the inconsistency of training and inference results can lead to deviations when AI deals with high-precision factual questions.

How can this problem be solved? A paper has been published on the Cornell University ArXiv platform proposing a method to improve the reliability of LLM results through validation by multiple models.

In simple terms, it means first allowing the main model to generate results, and then integrating multiple validation models to conduct a “majority voting analysis” on the issue, thereby reducing the “hallucinations” produced 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 that 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 specifically built for the verification of AI LLMs, which constructs a reliable verification layer between users and the underlying AI models.

With the existence of this validation layer network, integrated services can be realized, including privacy protection, accuracy assurance, scalable design, and standardized API interfaces. It can enhance the feasibility of AI in various niche application scenarios by reducing the hallucinations produced by AI LLMs, and it is also a practical application of the Crypto distributed validation network 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, the Gigabrain trading platform can add an extra layer to verify the accuracy of market analysis and predictions, filtering out unreliable suggestions, which can improve the accuracy of AI trading signals and make the application of AI LLMs in DeFi scenarios more reliable.

2. Learnrite uses Mira to validate AI-generated standardized exam questions, enabling educational institutions to leverage AI-generated content on a large scale without compromising the accuracy of educational assessments, thus maintaining strict educational standards.

3. The blockchain Kernel project utilizes Mira’s LLM consensus mechanism, integrating it into the BNB ecosystem to create a decentralized validation network (DVN), which ensures a certain degree of accuracy and security in executing AI computations on the blockchain.

Above.

In fact, Mira Network provides middleware consensus network services, which is certainly not the only way to enhance AI application capabilities. In reality, there are alternative paths such as enhancing through data-side training, enhancing through interactions with multimodal large models, and enhancing privacy computing with potential cryptographic technologies like ZKP, FHE, TEE, etc. However, compared to these, Mira’s solution is valuable for its rapid implementation and direct effectiveness.

Note: I am particularly interested in the technical concept of this project. From a technical perspective, it can indeed address the application landing issues currently faced by AI LLMs, and it can also demonstrate the supplementary value of the Crypto distributed consensus network. Whether it is worth participating in the experience or if it is a potential opportunity for profit, everyone can make their own judgment. (Public test experience entrance