  Recently, decoder-only pre-trained large language models (LLMs), with several
tens of billion parameters, have significantly impacted a wide range of natural
language processing (NLP) tasks. While encoder-only or encoder-decoder
pre-trained language models have already proved to be effective in discourse
parsing, the extent to which LLMs can perform this task remains an open
research question. Therefore, this paper explores how beneficial such LLMs are
for Rhetorical Structure Theory (RST) discourse parsing. Here, the parsing
process for both fundamental top-down and bottom-up strategies is converted
into prompts, which LLMs can work with. We employ Llama 2 and fine-tune it with
QLoRA, which has fewer parameters that can be tuned. Experimental results on
three benchmark datasets, RST-DT, Instr-DT, and the GUM corpus, demonstrate
that Llama 2 with 70 billion parameters in the bottom-up strategy obtained
state-of-the-art (SOTA) results with significant differences. Furthermore, our
parsers demonstrated generalizability when evaluated on RST-DT, showing that,
in spite of being trained with the GUM corpus, it obtained similar performances
to those of existing parsers trained with RST-DT.
