  While search is the predominant method of accessing information, formulating
effective queries remains a challenging task, especially for situations where
the users are not familiar with a domain, or searching for documents in other
languages, or looking for complex information such as events, which are not
easily expressible as queries. Providing example documents or passages of
interest, might be easier for a user, however, such query-by-example scenarios
are prone to concept drift, and are highly sensitive to the query generation
method. This demo illustrates complementary approaches of using LLMs
interactively, assisting and enabling the user to provide edits and feedback at
all stages of the query formulation process. The proposed Query Generation
Assistant is a novel search interface which supports automatic and interactive
query generation over a mono-linguial or multi-lingual document collection.
Specifically, the proposed assistive interface enables the users to refine the
queries generated by different LLMs, to provide feedback on the retrieved
documents or passages, and is able to incorporate the users' feedback as
prompts to generate more effective queries. The proposed interface is a
valuable experimental tool for exploring fine-tuning and prompting of LLMs for
query generation to qualitatively evaluate the effectiveness of retrieval and
ranking models, and for conducting Human-in-the-Loop (HITL) experiments for
complex search tasks where users struggle to formulate queries without such
assistance.
