# RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment

arXiv: https://arxiv.org/abs/2512.24943 · HTML: https://mlsanity.com/paper/2512.24943/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 31 Dec 2025.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain](https://mlsanity.com/paper/2401.16822/) (2024)
- [Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring](https://mlsanity.com/paper/1905.01969/) (2019)
- [StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding](https://mlsanity.com/paper/1908.04577/) (2019)
- [EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce](https://mlsanity.com/paper/2308.06966/) (2023)
- [MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity](https://mlsanity.com/paper/2407.15838/) (2024)
- [Semantic Modelling with Long-Short-Term Memory for Information Retrieval](https://mlsanity.com/paper/1412.6629/) (2014)
- [A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval](https://mlsanity.com/paper/2303.15870/) (2023)
- [Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search](https://mlsanity.com/paper/2210.01701/) (2022)
- [EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-commerce Models](https://mlsanity.com/paper/2508.15721/) (2025)
- [Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce](https://mlsanity.com/paper/2407.09395/) (2024)
- [BloomVQA: Assessing Hierarchical Multi-modal Comprehension](https://mlsanity.com/paper/2312.12716/) (2023)
- [ADORE: Autonomous Domain-Oriented Relevance Engine for E-commerce](https://mlsanity.com/paper/2512.02555/) (2025)

## Abstract (arXiv)

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 on challenging cases to assess performance limits; (3) a visual salience subset for evaluating multimodal understanding capabilities. We conducted experiments on RAIR using 14 open and closed-source models. The results demonstrate that RAIR presents sufficient challenges even for GPT-5, which achieved the best performance. RAIR data are now available, serving as an industry benchmark for relevance assessment while providing new insights into general LLM and Visual Language Model(VLM) evaluation.