ChatGPT is a Potential Zero-Shot Dependency Parser
Boda Lin Xinyi Zhou Binghao Tang Xiaocheng Gong Si Li
Pre-trained language models have been widely used in dependency parsing task
and have achieved significant improvements in parser performance. However, it
remains an understudied question whether pre-trained language models can
spontaneously exhibit the ability of dependency parsing without introducing
additional parser structure in the zero-shot scenario. In this paper, we
propose to explore the dependency parsing ability of large language models such
as ChatGPT and conduct linguistic analysis. The experimental results
demonstrate that ChatGPT is a potential zero-shot dependency parser, and the
linguistic analysis also shows some unique preferences in parsing outputs.