IntellectNavigator: Enhancing Search Tools with LLMs-Powered Query Instruction
Dung Vo · 2024
In the rapidly evolving landscape of information retrieval (IR), the integration of Large Language Models (LLMs) has opened new avenues for enhancing search engine interactions. This work presents a novel perspective on harnessing the capabilities of LLMs to transform search engines from mere tools into intelligent platforms that actively guide users in formulating and refining their queries. This paper explores the intersection of AI and IR, focusing on how AI can act as a mentor, helping users navigate the complex information ecosystem more effectively. We propose a framework where LLMs not only understand and interpret user intents, but also dynamically adapt queries for optimal search outcomes. This approach promises to make information retrieval more intuitive, personalized, and efficient, paving the way for a new era in search technology.