تقرير
LLM-Oriented Retrieval Tuner
العنوان: | LLM-Oriented Retrieval Tuner |
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المؤلفون: | Sun, Si, Zhang, Hanqing, Liu, Zhiyuan, Bao, Jie, Song, Dawei |
سنة النشر: | 2024 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computation and Language |
الوصف: | 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. Comment: 16 pages, 8 figures, 5 tables |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2403.01999 |
رقم الأكسشن: | edsarx.2403.01999 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |