Probabilistic Inference Layer Integration in Mistral LLM for Accurate Information Retrieval

Abstract

This study introduces a novel integration of a Probabilistic Inference Layer (PIL) into the Mistral Large Language Model (LLM), aiming to address the critical challenge of accurate and reliable information retrieval in natural language processing. By employing advanced statistical models within the PIL, the enhanced Mistral LLM demonstrates a marked improvement in information retrieval accuracy, context understanding, and bias reduction. The PIL's application of Bayesian networks and sophisticated mathematical constructs, such as matrix calculus and principles akin to Bernoulli's and Lorentz transformations, enables the Mistral LLM to process information with a higher degree of accuracy and reliability. The study's results indicate significant advancements in the model's performance across various tests, particularly in discerning context and intent, reducing biases, and handling complex logical operations. Despite its computational demands and the ongoing challenge to completely eliminate biases, the PIL integration establishes a new benchmark for LLMs and opens avenues for future research. This study contributes to the field by demonstrating the potential of probabilistic methods in enhancing the capabilities of generative AI models, thus paving the way for more sophisticated and reliable AI-driven information systems.