@inproceedings{jain-etal-2026-probes,
    title = "{PROBES} : Performance and Relevance Observation for {BE}tter Search",
    author = "Jain, Sejal  and
      DSouza, Cyrus Andre  and
      Rana, Jitenkumar Babubhai  and
      Joshi, Aniket  and
      Yenigalla, Promod",
    editor = {Matusevych, Yevgen  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Aletras, Nikolaos},
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-industry.48/",
    pages = "625--635",
    ISBN = "979-8-89176-384-5",
    abstract = "High-quality search is essential for the success of online platforms, spanning e-commerce, social media, shopping-focused applications, and broader search systems such as content discovery and enterprise web search. To ensure optimal user experience and drive business growth, continuous evaluation and improvement of search systems is crucial. This paper introduces PROBES, a novel multi-task system powered by Large Language Models (LLMs) designed for end-to-end evaluation of semantic search systems. PROBES identifies context-aware relevance using a fine-grained scale (exact, substitute, complement, irrelevant) by leveraging the query category, feature-level intent, and category-aware feature importance, enabling more precise and consistent judgments than relying solely on raw query text. This allows PROBES to provide differentiated relevance assessment across a diverse range of query categories. PROBES then dives deeper to understand the reason behind irrelevant results (Precision issues) by checking product content conflicts and inaccuracies. It also analyzes Missed Recall by leveraging retrieval and relevance models to determine whether a missed recall was due to a selection issue or a ranking/retrieval system issue. To evaluate PROBES, we introduce a new metric, the Actionable Error Rate (AER), defined as the proportion of actionable errors over all flagged errors. We observe that PROBES operates at an AER of 76{\%}, generating actionable insights across 100 product categories."
}