From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance Prediction
In this work, we propose a framework that bridges this gap by distilling a high-performing LLM-based relevance judgment system into efficient multimodal student models.
ProofPaper ↗
Key points
- Relevance prediction between user queries and items is a fundamental component of e-commerce search systems.
- Recent advances in large language models (LLMs) enable automated relevance judgment at scale, achieving performance comparable to human annotators.
- Using this dataset, we develop a reliable LLM-as-a-judge pipeline through careful prompt engineering and iterative refinement.
- This approach enables practical deployment of high-quality relevance prediction systems in production environments.
Sources (1)
- [1]From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance PredictionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 10:50 AM
In this work, we propose a framework that bridges this gap by distilling a high-performing LLM-based relevance judgment system into efficient multimodal student models.
Relevance prediction between user queries and items is a fundamental component of e-commerce search systems.
Extractive summary: sentences quoted from the sources.
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