Linking a claim to grounded references is a critical ability to fulfill human
demands for authentic and reliable information. Current studies are limited to
specific tasks like information retrieval or semantic matching, where the
claim-reference relationships are unique and fixed, while the referential
knowledge linking (RKL) in real-world can be much more diverse and complex. In
this paper, we propose universal referential knowledge linking (URL), which
aims to resolve diversified referential knowledge linking tasks by one unified
model. To this end, we propose a LLM-driven task-instructed representation
compression, as well as a multi-view learning approach, in order to effectively
adapt the instruction following and semantic understanding abilities of LLMs to
referential knowledge linking. Furthermore, we also construct a new benchmark
to evaluate ability of models on referential knowledge linking tasks across
different scenarios. Experiments demonstrate that universal RKL is challenging
for existing approaches, while the proposed framework can effectively resolve
the task across various scenarios, and therefore outperforms previous
approaches by a large margin.

本文提出了一种统一的参考知识链接模型 (Universal Referential Knowledge Linking, URL)，通过 LLM 驱动的任务指导表示压缩和多视角学习方法，有效地将 LLM 的指令跟随和语义理解能力应用于参考知识链接，以解决多样化的参考知识链接任务。实验结果表明，现有方法在面对普适 RKL 时存在挑战，而所提出的框架能够有效地解决不同场景下的任务，从而大幅度优于先前的方法。