LLM-Oriented Retrieval Tuner

• URL: http://arxiv.org/abs/2403.01999v1
• Date: Mon, 4 Mar 2024 12:50:25 GMT
• Title: LLM-Oriented Retrieval Tuner
• Authors: Si Sun, Hanqing Zhang, Zhiyuan Liu, Jie Bao, Dawei Song
• Abstract summary: Dense Retrieval (DR) is now considered as a promising tool to enhance the memorization capacity of Large Language Models (LLM) We propose an efficient LLM-Oriented Retrieval Tuner, namely LMORT, which decouples DR capacity from base LLM. Our approach could achieve competitive zero-shot retrieval performance compared to a range of strong DR models.
• Score: 25.563739811422874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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