Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback
Yujia Zhou,
Zhicheng Dou,
Ji-Rong Wen
Abstract:The recent advent of end-to-end generative retrieval marks a significant shift in document retrieval methods, leveraging differentiable search indexes to directly produce relevant document identifiers (docids) in response to a specific query. Nevertheless, this approach faces two fundamental challenges: (i) a discrepancy between the token-level probabilistic optimization and the broader document-level relevance estimation; (ii) an overemphasis on top-1 results at the expense of overall ranking quality. To tack… Show more
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