Integrating Deep Learning with Symbolic Reasoning in TinyLlama for Accurate Information Retrieval

Abstract

This study presents a novel approach to enhancing information retrieval capabilities in Large Language Models (LLMs) by integrating deep learning with symbolic reasoning, specifically in the TinyLlama model. The research addresses the inherent limitations of LLMs in processing contextually complex queries and ensuring factual accuracy. By amalgamating the intuitive pattern recognition of deep learning with structured, rule-based logic of symbolic reasoning, the improved TinyLlama model demonstrates a significant elevation in performance. The study employs the BIG-bench benchmark tasks to empirically validate the model's enhancements in accuracy, logical consistency, and rule adherence. Additionally, the research emphasizes the importance of model interpretability and trust, positioning the hybrid model as a more transparent and reliable AI tool. The findings not only showcase the efficacy of the hybrid architecture but also pave the way for future AI research, focusing on sophisticated cognitive functions and autonomous adaptation in dynamic environments. This work sets a precedent in the evolution of LLMs, moving towards AI systems capable of nuanced reasoning akin to human cognitive processes.