Enhancing Long-Term Capabilities of Large Language Models via Discourse Sub-graph Analysis

Abstract

The rapid advancement of Large Language ModelS (LLMs) has inaugurated a transformative era in natural language processing, fostering unprecedented capabilities in text generation, understanding, and contextual analysis. However, effectively handling extensive contexts, which are crucial for many applications, remains a significant challenge due to the intrinsic limitations of the context window sizes of the models and the computational burdens associated with their operations. This study proposes an innovative framework that uses unsupervised natural language summarization to provide more efficient context handling. Our methodology is dubbed LONGPC. We demonstrate that our framework significantly reduces computational overhead and enhances LLMs’ performance across various datasets while maintaining or even enhancing the quality of the generated content.