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Large language models (LLMs) have demonstrated remarkable capabilities acrossvarious research domains, including the field of Information Retrieval (IR).However, the responses generated by off-the-shelf LLMs tend to be generic,i.e., cannot capture the distinctiveness of each document with similar content.This limits the performance of LLMs in IR because finding and distinguishingrelevant documents from substantial similar documents is a typical problem inmany IR tasks. To address this issue, we propose an unsupervised alignmentmethod, namely Reinforcement Learning from Contrastive Feedback (RLCF),empowering LLMs to generate both high-quality and context-specific responses.Our approach constructs unsupervised contrastive feedback signals based onsimilar document groups, and adopts a reward function, named group-wisereciprocal rank, to optimize LLMs within a standard Proximal PolicyOptimization. We conduct extensive experiments to evaluate the effectiveness ofRLCF on LLMs built with different languages and parameter sizes on multipledownstream IR applications. RLCF significantly outperforms existing alignmentmethods, and RLCF-optimized LLMs demonstrate considerable improvement ingenerating responses with distinctiveness.

Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback