Everyone is aware 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 practical application scenarios. How can this be solved? Recently, @Mira_Network launched a public testnet and provided a solution. Let me explain what’s going on:
First, the occurrence of "hallucinations" in AI large language model (LLM) tools is something everyone can perceive, mainly for two reasons:
The training data for AI LLMs is not comprehensive enough. Although the data scale is quite large, it still cannot cover information in 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 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 validation by multiple models.
Simply put, the main model first generates results, and then multiple validation models 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 validation platform is certainly needed to manage and verify the collaborative interaction process between the main model and the validation models. Mira Network is such a middleware network specifically built for validating AI LLMs, creating a reliable validation layer between users and the underlying AI models.
With the existence of this validation layer network, integrated services can be achieved, including privacy protection, accuracy assurance, scalable design, and standardized API interfaces. This can expand the feasibility of AI in various segmented application scenarios by reducing the hallucinations of AI LLMs, and it is 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 to support this:
1) After integrating Mira, the Gigabrain trading platform can add a layer of verification to 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 utilizes Mira to validate 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 integrated Mira's LLM consensus mechanism into the BNB ecosystem, creating a decentralized validation network (DVN) that ensures a certain level of accuracy and security for AI computations executed on the blockchain.
That’s all.
In fact, what Mira Network provides is a middleware consensus network service, which is certainly not the only way to enhance AI application capabilities. In fact, there are alternative paths such as enhancing through data training, enhancing through interactions with multimodal large models, and enhancing through privacy computing with potential cryptographic technologies like ZKP, FHE, TEE, etc. However, compared to these, Mira's solution is valuable for its quick practical implementation and immediate effectiveness.
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