When Search Engine Services meet Large Language Models: Visions and Challenges

TL;DR

This paper investigates the integration of Large Language Models (LLMs) with search engine services, proposing two complementary visions: Search4LLM, where search engines supply data, tasks, and signals to improve LLM pre-training, fine-tuning, and alignment; and LLM4Search, where LLMs enhance search engines through advanced query rewriting, information extraction, and retrieval optimization. It details concrete mechanisms such as Retrieval-Augmented Generation (RAG), Learning-to-Rank (LTR), domain-specific fine-tuning, and cross-domain QA, while outlining evaluation frameworks and real-time data integration. The authors also discuss critical challenges, including biases, computational costs, data freshness, explainability, and privacy, and chart research directions to address them. The proposed framework aims to enable scalable, user-centric, and responsible intelligent search services that leverage the strengths of both LLMs and search engines, potentially transforming service computing and information retrieval.

Abstract

Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we search for and retrieve information, understand content, and interact with internet services. This paper conducts an in-depth examination of how integrating LLMs with search engines can mutually benefit both technologies. We focus on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engine functions using LLMs (LLM4Search). For Search4LLM, we investigate how search engines can provide diverse high-quality datasets for pre-training of LLMs, how they can use the most relevant documents to help LLMs learn to answer queries more accurately, how training LLMs with Learning-To-Rank (LTR) tasks can enhance their ability to respond with greater precision, and how incorporating recent search results can make LLM-generated content more accurate and current. In terms of LLM4Search, we examine how LLMs can be used to summarize content for better indexing by search engines, improve query outcomes through optimization, enhance the ranking of search results by analyzing document relevance, and help in annotating data for learning-to-rank tasks in various learning contexts. However, this promising integration comes with its challenges, which include addressing potential biases and ethical issues in training models, managing the computational and other costs of incorporating LLMs into search services, and continuously updating LLM training with the ever-changing web content. We discuss these challenges and chart out required research directions to address them. We also discuss broader implications for service computing, such as scalability, privacy concerns, and the need to adapt search engine architectures for these advanced models.

Figures (11)

• Figure 1: Technological Evolution of AI Models and Search Engine Technologies: Some of the key milestones achieved by AI and search engine (information retrieval) technologies.
• Figure 2: Architectural Design, Essential Components with Functionalities of a Common Search Engine Service
• Figure 3: The Life-cycle of LLMs: Pre-training, supervised fine-tuning, model algiments with human feedback, and building applications with agents.
• Figure 4: An Overview of Search4LLM Theme: leveraging the search engine functionalities to process the data crawled from web and responses from LLMs, providing datasets for pre-training, supervised fine-tuning, and model alignments.
• Figure 5: Extracting Questions and Answers for SFT from Search Queries and Top Search Results