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    <abstract>The ability to generate counterarguments is important for critical thinking and balanced discourse, yet existing approaches typically produce only a single counterargument, failing to capture the diversity and persuasiveness required in real-world debates. 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, PTCG operationalizes perspective-taking and enables the generation of diverse counterarguments. Results from LLM-as-a-Judge, classifier-based assessment, and human evaluations indicate that PTCG shows consistent improvements in both the diversity and persuasiveness of counterarguments compared to baseline methods.</abstract>
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%0 Conference Proceedings
%T PTCG: Persona-guided Tree-based Counterargument Generation
%A Son, Eunbeen
%A Jo, Yohan
%A Park, Joonsuk
%A Bak, JinYeong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F son-etal-2026-ptcg
%X The ability to generate counterarguments is important for critical thinking and balanced discourse, yet existing approaches typically produce only a single counterargument, failing to capture the diversity and persuasiveness required in real-world debates. 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, PTCG operationalizes perspective-taking and enables the generation of diverse counterarguments. Results from LLM-as-a-Judge, classifier-based assessment, and human evaluations indicate that PTCG shows consistent improvements in both the diversity and persuasiveness of counterarguments compared to baseline methods.
%U https://aclanthology.org/2026.findings-emnlp.133/
%P 2705-2734

Markdown (Informal)

[PTCG: Persona-guided Tree-based Counterargument Generation](https://aclanthology.org/2026.findings-emnlp.133/) (Son et al., Findings 2026)

• PTCG: Persona-guided Tree-based Counterargument Generation (Son et al., Findings 2026)

ACL

• Eunbeen Son, Yohan Jo, Joonsuk Park, and JinYeong Bak. 2026. PTCG: Persona-guided Tree-based Counterargument Generation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2705–2734, Budapest, Hungary. Association for Computational Linguistics.