Although achieving great success, Large Language Models (LLMs) usually suffer
from unreliable hallucinations. In this paper, we define a new task of
Knowledge-aware Language Model Attribution (KaLMA) that improves upon three
core concerns on conventional attributed LMs. First, we extend attribution
source from unstructured texts to Knowledge Graph (KG), whose rich structures
benefit both the attribution performance and working scenarios. Second, we
propose a new ``Conscious Incompetence" setting considering the incomplete
knowledge repository, where the model identifies the need for supporting
knowledge beyond the provided KG. Third, we propose a comprehensive automatic
evaluation metric encompassing text quality, citation quality, and text
citation alignment. To implement the above innovations, we build a dataset in
biography domain BioKaLMA via a well-designed evolutionary question generation
strategy, to control the question complexity and necessary knowledge to the
answer. For evaluation, we develop a baseline solution and demonstrate the room
for improvement in LLMs' citation generation, emphasizing the importance of
incorporating the "Conscious Incompetence" setting, and the critical role of
retrieval accuracy.

大型语言模型（LLMs）在可靠性方面通常存在不可靠的幻觉。在本文中，我们定义了一项名为知识感知语言模型归因（KaLMA）的新任务，它改进了传统归因语言模型的三个核心问题。