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This research introduces PTCG, a framework that enhances critical thinking by generating diverse and persuasive counterarguments through persona-guided Tree-of-Thoughts methods. By simulating distinct speaker perspectives, the model addresses limitations in current single-argument generation approaches, showing measurable improvements in both diversity and persuasiveness. The work represents a significant step forward in advancing AI capabilities for balanced discourse and complex debate simulation.
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PTCG: Persona-guided Tree-based Counterargument Generation

arXiv2 min read

arXiv:2609.07120v1 Announce Type: new 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: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.

Accepted to Findings of EMNLP 2026. 30 pages, 13 figures, 15 tables

Computation and Language (cs.CL)

arXiv:2609.07120 [cs.CL]

(or arXiv:2609.07120v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.07120 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 216 words

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