Title: PTCG: Persona-guided Tree-based Counterargument Generation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Counterargument, Generation, Persona

Abstract: The ability to generate counterarguments is important for fostering critical thinking, balanced discourse, and informed decision-making.
However, existing approaches typically produce only a single counterargument, thereby overlooking the diversity and persuasiveness required in real-world debates.
This limitation is critical, as the same topic may persuade different individuals only when framed from distinct perspectives.
To address this limitation, we propose Persona-guided Tree-based Counterargument Generation (PTCG), a framework that combines Tree-of-Thoughts–inspired step-wise generation and pruning with speaker persona selection.
By estimating the author’s persona from the original argument and incorporating speaker personas representing distinct perspectives, the framework operationalizes perspective-taking, enabling reasoning from multiple standpoints and supporting the generation of diverse counterarguments.
We propose a tree-based procedure that generates plans, selects the best, and produces multiple speaker persona-specific counterarguments, from which the most effective are chosen.
We evaluate PTCG through a comprehensive multi-faceted setup, combining Large Language Model (LLM)-as-a-Judge, classifier-based assessment, and human evaluations.
Our experimental results show that PTCG substantially improves both the diversity and persuasiveness of counterarguments compared to baselines.
These findings highlight the effectiveness of adaptive persona integration in boosting diversity and strengthening persuasiveness.