RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators (2205.12590v1)

Abstract: In this paper, we study the task of improving the cohesion and coherence of long-form text generated by LLMs. To this end, we propose RSTGen, a framework that utilises Rhetorical Structure Theory (RST), a classical language theory, to control the discourse structure, semantics and topics of generated text. Firstly, we demonstrate our model's ability to control structural discourse and semantic features of generated text in open generation evaluation. Then we experiment on the two challenging long-form text tasks of argument generation and story generation. Evaluation using automated metrics and a metric with high correlation to human evaluation, shows that our model performs competitively against existing models, while offering significantly more controls over generated text than alternative methods.