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.

翻译：将主张与已证实参照进行链接是实现真实可靠信息需求的关键能力。现有研究局限于信息检索或语义匹配等特定任务，其中主张-参照关系具有唯一性和固定性，而现实世界中的参照知识链接（RKL）却更为多样复杂。本文提出通用参照知识链接（URL），旨在通过统一模型解决多样化的参照知识链接任务。为此，我们提出基于大语言模型的任务引导表示压缩方法，结合多视角学习策略，有效将大语言模型的指令遵循和语义理解能力适配至参照知识链接任务。此外，我们构建了跨场景评估模型参照知识链接能力的新基准。实验证明，现有方法难以应对通用参照知识链接任务，而所提框架能有效解决多种场景下的任务，显著超越现有方法。