Refining Large Language Model Query Optimization: An Adaptive Semantic Approach

This paper presents a novel approach to improve how questions interact with LLMs in this paper is presented. To this end, we developed an index called Query Semantic Complexity (QSC) that quantifies how challenging a question is. We also developed a method by the name Adaptive Semantic Query Optimization (ASQO) which alters the manner that it processes questions depending on their level of difficulty. Our approach attempts to try striking the middle ground between providing exact responses and employing the computer resources. Our concepts were tried on various LLMs such as GPT-3, T511B, as well as BERT-large that we discussed in this work. The results included great enhancements in speed in its response to questions, and precision of the answers provided. We also applied our method to examples from science and technical writing in practice. It turned out to be most effective when dealing with challenging questions and with large language generators. Overall this research provide a promising approach to making the LLMs perform better when responding to questions.