  We audited large language models (LLMs) for their ability to create
evidence-based and stylistic counter-arguments to posts from the Reddit
ChangeMyView dataset. We benchmarked their rhetorical quality across a host of
qualitative and quantitative metrics and then ultimately evaluated them on
their persuasive abilities as compared to human counter-arguments. Our
evaluation is based on Counterfire: a new dataset of 32,000 counter-arguments
generated from large language models (LLMs): GPT-3.5 Turbo and Koala and their
fine-tuned variants, and PaLM 2, with varying prompts for evidence use and
argumentative style. GPT-3.5 Turbo ranked highest in argument quality with
strong paraphrasing and style adherence, particularly in `reciprocity' style
arguments. However, the stylistic counter-arguments still fall short of human
persuasive standards, where people also preferred reciprocal to evidence-based
rebuttals. The findings suggest that a balance between evidentiality and
stylistic elements is vital to a compelling counter-argument. We close with a
discussion of future research directions and implications for evaluating LLM
outputs.
