  Constituency parsing is a fundamental yet unsolved natural language
processing task. In this paper, we explore the potential of recent large
language models (LLMs) that have exhibited remarkable performance across
various domains and tasks to tackle this task. We employ three linearization
strategies to transform output trees into symbol sequences, such that LLMs can
solve constituency parsing by generating linearized trees. We conduct
experiments using a diverse range of LLMs, including ChatGPT, GPT-4, OPT,
LLaMA, and Alpaca, comparing their performance against the state-of-the-art
constituency parsers. Our experiments encompass zero-shot, few-shot, and
full-training learning settings, and we evaluate the models on one in-domain
and five out-of-domain test datasets. Our findings reveal insights into LLMs'
performance, generalization abilities, and challenges in constituency parsing.
