AUTHOR=Baysan Mehmet Selman , Uysal Serkan , İşlek İrem , Çığ Karaman Çağla , Güngör Tunga 

TITLE=LLM-as-a-Judge: automated evaluation of search query parsing using large language models

JOURNAL=Frontiers in Big Data

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1611389

DOI=10.3389/fdata.2025.1611389

ISSN=2624-909X

ABSTRACT=IntroductionThe adoption of Large Language Models (LLMs) in search systems necessitates new evaluation methodologies beyond traditional rule-based or manual approaches.MethodsWe propose a general framework for evaluating structured outputs using LLMs, focusing on search query parsing within an online classified platform. Our approach leverages LLMs' contextual reasoning capabilities through three evaluation methodologies: Pointwise, Pairwise, and Pass/Fail assessments. Additionally, we introduce a Contextual Evaluation Prompt Routing strategy to improve reliability and reduce hallucinations.ResultsExperiments conducted on both small- and large-scale datasets demonstrate that LLM-based evaluation achieves approximately 90% agreement with human judgments.DiscussionThese results validate LLM-driven evaluation as a scalable, interpretable, and effective alternative to traditional evaluation methods, providing robust query parsing for real-world search systems.