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[2512.24943] RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment
Summary
The RAIR benchmark introduces a comprehensive dataset for evaluating e-commerce relevance, addressing the limitations of existing benchmarks and enhancing model assessment capabilities.
Why It Matters
As e-commerce continues to grow, the need for effective relevance assessment in search engines becomes critical. RAIR provides a standardized framework and dataset that can improve the evaluation of relevance models, ensuring they meet industry demands and enhance user experience.
Key Takeaways
- RAIR offers a standardized benchmark for assessing e-commerce relevance.
- The dataset includes three subsets: general, long-tail hard, and visual salience.
- RAIR challenges even advanced models like GPT-5, highlighting its robustness.
- The benchmark aims to unify evaluation metrics across the industry.
- RAIR's insights can guide future developments in relevance models.
Computer Science > Information Retrieval arXiv:2512.24943 (cs) [Submitted on 31 Dec 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment Authors:Chenji Lu, Zhuo Chen, Hui Zhao, Zhenyi Wang, Pengjie Wang, Chuan Yu, Jian Xu View a PDF of the paper titled RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment, by Chenji Lu and 6 other authors View PDF HTML (experimental) Abstract:Search relevance plays a central role in web e-commerce. While large language models (LLMs) have shown significant results on relevance task, existing benchmarks lack sufficient complexity for comprehensive model assessment, resulting in an absence of standardized relevance evaluation metrics across the industry. To address this limitation, we propose Rule-Aware benchmark with Image for Relevance assessment(RAIR), a Chinese dataset derived from real-world scenarios. RAIR established a standardized framework for relevance assessment and provides a set of universal rules, which forms the foundation for standardized evaluation. Additionally, RAIR analyzes essential capabilities required for current relevance models and introduces a comprehensive dataset consists of three subset: (1) a general subset with industry-balanced sampling to evaluate fundamental model competencies; (2) a long-tail hard subset focus o...
