An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback

• URL: http://arxiv.org/abs/2311.11226v1
• Date: Sun, 19 Nov 2023 04:42:24 GMT
• Title: An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback
• Authors: Kaustubh D. Dhole, Ramraj Chandradevan, Eugene Agichtein
• Abstract summary: The proposed interface is a novel search interface which supports automatic and interactive query generation over a mono-linguial or multi-lingual document collection. The 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.
• Score: 9.461978375200102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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