Can Mira Network solve the "hallucination" problem of AI large models?

Mira Network is a middleware network specifically designed to validate AI LLMs, creating a reliable verification layer between users and the underlying AI models.

Written by: Haotian

Everyone knows that the biggest obstacle for large AI 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 by actual application scenarios. How to solve it? Recently, @Mira_Network launched a public testnet and provided a solution. Let me explain what’s going on:

First, there is a phenomenon of “hallucination” in AI large model tools that everyone can perceive, mainly for two reasons:

1. AI LLMs training data is not comprehensive enough; although the existing data scale is vast, it still cannot cover information from some niche or specialized fields. At this point, AI tends to make “creative completions,” which can lead to some real-time errors.

2. The nature 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, inconsistencies in training and inference results, etc., can lead to deviations in AI when addressing high-precision factual issues.

How can this problem be solved? A paper published on the Cornell University ArXiv platform presents a method to improve the reliability of LLM results through the validation of 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 this case, a distributed verification platform is definitely needed to manage and verify the collaborative interaction process between the master model and the verification model, and Mira Network is such a middleware network that specializes in building AI LLMs verification, building a reliable verification layer between users and the underlying AI model.

With the existence of this verification layer network, integrated services can be realized, including privacy protection, accuracy assurance, scalable design, and standardized API interfaces. By reducing the hallucinations output by AI LLMs, it can expand the feasibility of AI in various segmented application scenarios, and it 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 Gigabrain integrates with Mira, the system can add an additional layer to verify the accuracy of market analysis and predictions, filtering out unreliable suggestions, which can improve the accuracy of AI trading signals, making AI LLMs more reliable in DeFi scenarios.

2. Learnrite utilizes Mira to verify the standardized exam questions generated by AI, enabling educational institutions to leverage AI-generated content on a large scale without compromising the content accuracy of educational assessments, thus maintaining 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 degree of accuracy and security for AI computations executed 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, enhancing through data-driven training, enhancing through interactions of multimodal large models, and enhancing through privacy computing using potential cryptographic technologies such as ZKP, FHE, and TEE are all optional paths. However, in comparison, Mira’s solution is valuable for its fast implementation in practice and direct effectiveness.