  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.
