A Principled Framework for Knowledge-enhanced Large Language Model

TL;DR

This paper proposes a structured framework for enhancing large language models with knowledge anchoring and closed-loop reasoning, aiming to improve their reliability and depth in complex tasks.

Contribution

It introduces a novel, theoretically grounded framework that systematically integrates knowledge anchoring and iterative reasoning into LLMs.

Findings

Framework improves reasoning accuracy under certain assumptions

Dissects component contributions to model performance

Provides theoretical guarantees for reasoning improvements

Abstract

Large Language Models (LLMs) are versatile, yet they often falter in tasks requiring deep and reliable reasoning due to issues like hallucinations, limiting their applicability in critical scenarios. This paper introduces a rigorously designed framework for creating LLMs that effectively anchor knowledge and employ a closed-loop reasoning process, enhancing their capability for in-depth analysis. We dissect the framework to illustrate the contribution of each component to the LLMs' performance, offering a theoretical assurance of improved reasoning under well-defined assumptions.