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Stanford NLP Shared Paper: Using Reinforcement Learning to Optimize Black-Box Document Retrieval
ME News Updates, April 8 (UTC+8), recently, a paper titled “Document Optimization for Black-Box Retrieval via Reinforcement Learning” authored by Omri Uzan, Ron Polonsky, Douwe Kiela, and Christopher Potts was shared. The study explores how to apply reinforcement learning techniques to optimize documents, aiming to improve the performance of black-box retrieval systems. The article suggests that this approach belongs to the research areas of computational linguistics and information retrieval. (Source: InFoQ)