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arXiv · 2403.01999

LLM-Oriented Retrieval Tuner

Si Sun·Hanqing Zhang·Zhiyuan Liu·Jie Bao·Dawei Song
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Abstract

Dense Retrieval (DR) is now considered as a promising tool to enhance the memorization capacity of Large Language Models (LLM) such as GPT3 and GPT-4 by incorporating external memories. However, due to the paradigm discrepancy between text generation of LLM and DR, it is still an open challenge to integrate the retrieval and generation tasks in a shared LLM. In this paper, we propose an efficient LLM-Oriented Retrieval Tuner, namely LMORT, which decouples DR capacity from base LLM and non-invasively coordinates the optimally aligned and uniform layers of the LLM towards a unified DR space, achieving an efficient and effective DR without tuning the LLM itself. The extensive experiments on six BEIR datasets show that our approach could achieve competitive zero-shot retrieval performance compared to a range of strong DR models while maintaining the generation ability of LLM.

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Si Sun, Hanqing Zhang, Zhiyuan Liu, Jie Bao, Dawei Song. 2024-03-04. LLM-Oriented Retrieval Tuner. https://arxiv.org/abs/2403.01999

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