# RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search Source: [RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search]() Collection index: [llms.txt]() Data updated: 2026-05-15T05:00:04.680Z Authors: Tingyu Chen, Wenkai Zhang, Li Gao, Lixin Su, Ge Chen, Dawei Yin, Daiting Shi Published: 2026-05-13T06:20:28.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Search Ranking on Baidu Search Live Production Traffic (14-day A/B test)](): Search Ranking; Baidu Search Live Production Traffic (14-day A/B test); Day Away (Median): -6.87. - [Search Ranking on Baidu Search Live Production Traffic High-Freshness queries (test)](): Search Ranking; Baidu Search Live Production Traffic High-Freshness queries (test); Day Away (Median): -12.81. ## Original sources and implementation [Original paper]() [PDF]()