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Evidence record 16147 · automatically gathered

Advancing Relevance Measurement with Vision-Language Models for Web-Scale Search

Relevance evaluation plays a crucial role in personalized search systems, serving as a guardrail alongside user engagement metrics to ensure that search results align with user queries and intent. While human annotation is the traditional method for relevance evaluation, its high cost and long turnaround time limit its scalability. In this work, we present a VLM-based automated relevance evaluation pipeline deployed within Pinterest Search for online A/B experiments. We rigorously validate the a

Record details

Published: 3 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment
Retrieved: 4 August 2026

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How to cite this record

ethics.ai (3 August 2026), “Advancing Relevance Measurement with Vision-Language Models for Web-Scale Search,” evidence record 16147, https://ethics.ai/record/16147 (originally published by arXiv cs.LG).

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