  Misinformation about climate change causes numerous negative impacts,
necessitating corrective responses. Psychological research has offered various
strategies for reducing the influence of climate misinformation, such as the
fact-myth-fallacy-fact-structure. However, practically implementing corrective
interventions at scale represents a challenge. Automatic detection and
correction of misinformation offers a solution to the misinformation problem.
This study documents the development of large language models that accept as
input a climate myth and produce a debunking that adheres to the
fact-myth-fallacy-fact (``truth sandwich'') structure, by incorporating
contrarian claim classification and fallacy detection into an LLM prompting
framework. We combine open (Mixtral, Palm2) and proprietary (GPT-4) LLMs with
prompting strategies of varying complexity. Experiments reveal promising
performance of GPT-4 and Mixtral if combined with structured prompts. We
identify specific challenges of debunking generation and human evaluation, and
map out avenues for future work. We release a dataset of high-quality
truth-sandwich debunkings, source code and a demo of the debunking system.
