The paper "When Search Engine Services meet Large Language Models: Visions and Challenges" by Haoyi Xiong et al. explores the integration of Large Language Models (LLMs) with search engine services, highlighting the mutual benefits and challenges of this integration. The authors focus on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engine functions using LLMs (LLM4Search). In the Search4LLM theme, the paper examines how search engines can provide diverse high-quality datasets for LLM pre-training, use relevant documents to help LLMs learn from queries, and incorporate Learning-To-Rank (LTR) tasks to enhance LLM precision. It also discusses how recent search results can make LLM-generated content more accurate and current. In the LLM4Search theme, the paper explores how LLMs can summarize content for better indexing, improve query outcomes through optimization, enhance search result ranking by analyzing document relevance, and assist in data annotation for LTR tasks. The integration of LLMs and search engines faces challenges such as addressing biases and ethical issues in model training, managing computational costs, and continuously updating LLM training with evolving web content. The paper discusses these challenges and outlines research directions to address them, emphasizing the broader implications for service computing, including scalability, privacy concerns, and the need to adapt search engine architectures for advanced models. The paper concludes by highlighting the significant advancements in both Search4LLM and LLM4Search themes, emphasizing the potential for creating smarter, more adaptive, and user-centric search services.The paper "When Search Engine Services meet Large Language Models: Visions and Challenges" by Haoyi Xiong et al. explores the integration of Large Language Models (LLMs) with search engine services, highlighting the mutual benefits and challenges of this integration. The authors focus on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engine functions using LLMs (LLM4Search). In the Search4LLM theme, the paper examines how search engines can provide diverse high-quality datasets for LLM pre-training, use relevant documents to help LLMs learn from queries, and incorporate Learning-To-Rank (LTR) tasks to enhance LLM precision. It also discusses how recent search results can make LLM-generated content more accurate and current. In the LLM4Search theme, the paper explores how LLMs can summarize content for better indexing, improve query outcomes through optimization, enhance search result ranking by analyzing document relevance, and assist in data annotation for LTR tasks. The integration of LLMs and search engines faces challenges such as addressing biases and ethical issues in model training, managing computational costs, and continuously updating LLM training with evolving web content. The paper discusses these challenges and outlines research directions to address them, emphasizing the broader implications for service computing, including scalability, privacy concerns, and the need to adapt search engine architectures for advanced models. The paper concludes by highlighting the significant advancements in both Search4LLM and LLM4Search themes, emphasizing the potential for creating smarter, more adaptive, and user-centric search services.