Generative Retrieval with Large Language Models

• URL: http://arxiv.org/abs/2402.17010v2
• Date: Tue, 29 Oct 2024 08:45:35 GMT
• Title: Generative Retrieval with Large Language Models
• Authors: Ye Wang, Xinrun Xu, Rui Xie, Wenxin Hu, Wei Ye,
• Abstract summary: This paper explores leveraging the parameterized knowledge stored during the pre-training phase of large language models to independently recall reference passage from any starting position. Experiments on KILT knowledge-sensitive tasks have verified that LLMs can independently recall reference passage location in various task forms.
• Score: 8.069852420775362
• License:
• Abstract: When completing knowledge-intensive tasks, humans sometimes need not just an answer but also a corresponding reference passage for auxiliary reading. Previous methods required obtaining pre-segmented article chunks through additional retrieval models. This paper explores leveraging the parameterized knowledge stored during the pre-training phase of large language models (LLMs) to independently recall reference passage from any starting position. We propose a two-stage framework that simulates the scenario of humans recalling easily forgotten references. Initially, the LLM is prompted to recall document title identifiers to obtain a coarse-grained document set. Then, based on the acquired coarse-grained document set, it recalls fine-grained passage. In the two-stage recall process, we use constrained decoding to ensure that content outside of the stored documents is not generated. To increase speed, we only recall a short prefix in the second stage, then locate its position to retrieve a complete passage. Experiments on KILT knowledge-sensitive tasks have verified that LLMs can independently recall reference passage location in various task forms, and the obtained reference significantly assist downstream tasks.

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