From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance Prediction
Relevance prediction between user queries and items is a fundamental component of e-commerce search systems. Traditionally, high-quality relevance labels are obtained through human judgment, which is reliable but expensive, slow, and difficult to scale. Recent advances in large language models (LLMs) enable automated relevance judgment at scale, achieving performance comparable to human annotators. However, such systems often rely on large commercial LLMs with complex prompts, resulting in high inference costs and limited throughput. In this work, we propose a framework that bridges this gap by distilling a high-performing LLM-based relevance judgment system into efficient multimodal stud...
| Authors | Tomasz Gniazdowski, Mohammadreza Momenifar, Nadia Vase et al. |
|---|---|
| Primary category | cs.IR |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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