  Although achieving great success, Large Language Models (LLMs) usually suffer
from unreliable hallucinations. Although language attribution can be a
potential solution, there are no suitable benchmarks and evaluation metrics to
attribute LLMs to structured knowledge. In this paper, we define a new task of
Knowledge-aware Language Model Attribution (KaLMA) that improves upon three
core concerns with 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 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.
