MindRef: Mimicking Human Memory for Hierarchical Reference Retrieval with Fine-Grained Location Awareness

When completing knowledge-intensive tasks, humans sometimes need an answer and a corresponding reference passage for auxiliary reading.Previous methods required obtaining presegmented 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 recall reference passage from any starting position independently.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 coarsegrained 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, and then locate its position to retrieve a complete passage.Experiments on KILT knowledgesensitive tasks have verified that LLMs can independently recall reference passage locations in various task forms, and the obtained reference significantly assists downstream tasks.