Towards Verifiable Generation: A Benchmark for Knowledge-aware Language
  Model Attribution
Xinze Li Yixin Cao2 Liangming Pan Yubo Ma Aixin Sun
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.