Web search engines have long served as indispensable tools for information retrieval; user behavior and query formulation strategies have been well studied. The introduction of search engines powered by large language models (LLMs) suggested more conversational search and new types of query strategies. In this paper, we compare traditional and LLM-based search for the task of image geolocation, i.e., determining the location where an image was captured. Our work examines user interactions, with a particular focus on query formulation strategies. In our study, 60 participants were assigned either traditional or LLM-based search engines as assistants for geolocation. Participants using traditional search more accurately predicted the location of the image compared to those using the LLM-based search. Distinct strategies emerged between users depending on the type of assistant. Participants using the LLM-based search issued longer, more natural language queries, but had shorter search sessions. When reformulating their search queries, traditional search participants tended to add more terms to their initial queries, whereas participants using the LLM-based search consistently rephrased their initial queries.

翻译：网络搜索引擎长期以来一直是信息检索不可或缺的工具；用户行为及查询构建策略已得到充分研究。基于大语言模型（LLM）的搜索引擎的出现带来了更具对话性的搜索方式及新型查询策略。本文针对图像地理定位任务（即确定图像拍摄地点）比较了传统搜索与基于大语言模型的搜索方法。我们的研究聚焦于用户交互行为，特别关注查询构建策略。在实验中，60名参与者被分配使用传统搜索引擎或基于大语言模型的搜索引擎作为地理定位辅助工具。结果表明，使用传统搜索引擎的参与者对图像拍摄地点的预测准确度高于使用基于大语言模型搜索的参与者。不同辅助工具的使用者展现出差异化的策略：使用基于大语言模型的参与者会输入更长、更自然的查询语句，但搜索会话时间较短。在重构查询时，传统搜索参与者倾向于在初始查询中添加更多术语，而基于大语言模型的搜索参与者则持续对初始查询进行重新表述。