Can Large Language Models Recall Reference Location Like Humans?
<|reference_start|>Can Large Language Models Recall Reference Location Like Humans?: 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.<|reference_end|>
@article{wang2024generative,
    title={Generative Retrieval with Large Language Models},
    author={Ye Wang, Xinrun Xu, Rui Xie, Wenxin Hu, Wei Ye},
    journal={arXiv preprint arXiv:2402.17010},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17010},
    primaryClass={cs.CL cs.AI}
}