Formulating effective search queries remains a challenging task, particularly when users lack expertise in a specific domain or are not proficient in the language of the content. Providing example documents of interest might be easier for a user. However, such query-by-example scenarios are prone to concept drift, and the retrieval effectiveness is highly sensitive to the query generation method, without a clear way to incorporate user feedback. To enable exploration and to support Human-In-The-Loop experiments we propose QueryExplorer -- an interactive query generation, reformulation, and retrieval interface with support for HuggingFace generation models and PyTerrier's retrieval pipelines and datasets, and extensive logging of human feedback. To allow users to create and modify effective queries, our demo supports complementary approaches of using LLMs interactively, assisting the user with edits and feedback at multiple stages of the query formulation process. With support for recording fine-grained interactions and user annotations, QueryExplorer can serve as a valuable experimental and research platform for annotation, qualitative evaluation, and conducting Human-in-the-Loop (HITL) experiments for complex search tasks where users struggle to formulate queries.

中文翻译：

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QueryExplorer：用于搜索和探索的交互式查询生成助手

制定有效的搜索查询仍然是一项具有挑战性的任务，特别是当用户缺乏特定领域的专业知识或不精通内容语言时。对于用户来说，提供感兴趣的示例文档可能会更容易。然而，这种按例查询的场景很容易出现概念漂移，并且检索效果对查询生成方法高度敏感，没有明确的方法来纳入用户反馈。为了实现探索并支持人机交互实验，我们提出了 QueryExplorer——一种交互式查询生成、重构和检索界面，支持 HuggingFace 生成模型和 PyTerrier 的检索管道和数据集，以及广泛的人类反馈记录。为了允许用户创建和修改有效的查询，我们的演示支持交互式使用 LLM 的补充方法，帮助用户在查询制定过程的多个阶段进行编辑和反馈。凭借对记录细粒度交互和用户注释的支持，QueryExplorer 可以作为一个有价值的实验和研究平台，用于注释、定性评估以及针对用户难以制定的复杂搜索任务进行人机交互 (HITL) 实验查询。