LLM-as-a-Judge: automated evaluation of search query parsing using large language models - Baysan, Mehmet Selman; Uysal, Serkan; İşlek, İrem and Çığ Karaman, Çağla and Güngör, Tunga (Frontiers in Big Data, 2025)
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.