ALCUNA: Large Language Models Meet New Knowledge
Xunjian Yin Baizhou Huang Xiaojun Wan
With the rapid development of NLP, large-scale language models (LLMs) excel
in various tasks across multiple domains now. However, existing benchmarks may
not adequately measure these models' capabilities, especially when faced with
new knowledge. In this paper, we address the lack of benchmarks to evaluate
LLMs' ability to handle new knowledge, an important and challenging aspect in
the rapidly evolving world. We propose an approach called KnowGen that
generates new knowledge by altering existing entity attributes and
relationships, resulting in artificial entities that are distinct from
real-world entities. With KnowGen, we introduce a benchmark named ALCUNA to
assess LLMs' abilities in knowledge understanding, differentiation, and
association. We benchmark several LLMs, reveals that their performance in face
of new knowledge is not satisfactory, particularly in reasoning between new and
internal knowledge. We also explore the impact of entity similarity on the
model's understanding of entity knowledge and the influence of contextual
entities. We appeal to the need for caution when using LLMs in new scenarios or
with new knowledge, and hope that our benchmarks can help drive the development
of LLMs in face of new knowledge.