Mitigating Token-Level Misalignment in Generative Information Retrieval via Reinforcement Learning from Human Feedback on

Abstract

Abstract: Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task, allowing for end-to-end optimization toward a unified global retrieval objective. However, existing GenIR models suffer from token-level misalignment, where models trained to predict the next token often fail to capture document-level relevance effectively. While reinforcement learning-based methods, such as reinforcement learning from relevance feedback (RLRF), aim to address this misalignment through reward modeling, they intro

Research Question

To what extent does reinforcement learning from human feedback mitigate token-level misalignment in generative information retrieval systems evaluated on cross-lingual datasets?

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