RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search

• URL: http://arxiv.org/abs/2605.13052v1
• Date: Wed, 13 May 2026 06:20:28 GMT
• Title: RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search
• Abstract summary: In commercial web search, aligning content freshness with user intent remains challenging due to the highly varied lifespans of information.<n>Traditional industrial approaches rely on static time-window filtering, resulting in "one-size-fits-all" rankings where content may be chronologically recent but semantically expired.<n>Our framework extracts fine-grained temporal contexts from documents to deduce a query-specific "validity horizon"-a semantic boundary defining when information becomes obsolete based on user intent.
• Score: 49.70446150612307
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In commercial web search, aligning content freshness with user intent remains challenging due to the highly varied lifespans of information. Traditional industrial approaches rely on static time-window filtering, resulting in "one-size-fits-all" rankings where content may be chronologically recent but semantically expired. To address the limitation, we present a novel Large Language Models (LLMs)-based Query-Aware Dynamic Content Expiration Prediction Framework deployed in Baidu search, reformulating timeliness as a dynamic validity inference task. Our framework extracts fine-grained temporal contexts from documents and leverages LLMs to deduce a query-specific "validity horizon"-a semantic boundary defining when information becomes obsolete based on user intent. Integrated with robust hallucination mitigation strategies to ensure reliability, our approach has been evaluated through offline and online A/B testing on live production traffic. Results demonstrate significant improvements in search freshness and user experience metrics, validating the effectiveness of LLM-driven reasoning for solving semantic expiration at an industrial scale.

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