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PTCG: Persona-guided Tree-based Counterargument Generation

Eunbeen Son, Yohan Jo, Joonsuk Park, JinYeong Bak

arXiv:2609.07120v1cs.CL

TL;DR

Existing counterargument-generation approaches often produce only one response and can miss the diversity and persuasiveness needed in real-world debates. PTCG estimates the author’s persona, selects distinct speaker personas, and uses stepwise plan generation and pruning. Evaluations report consistent improvements over baselines in diversity and persuasiveness.

  • Problem

    Existing approaches typically produce a single counterargument, limiting coverage of diverse perspectives, while generated arguments may lack value-based reasoning and scenario-driven perspectives important to persuasiveness.

    Source: §1 · p. 1

  • Method

    PTCG estimates the original author’s persona, selects distinct speaker personas, and uses Tree-of-Thoughts-inspired stepwise generation and pruning to produce persona-grounded counterarguments.

    Source: §1 · pp. 1–2

  • Results

    Across LLM-as-a-Judge, classifier-based, and human evaluations, PTCG consistently outperforms baselines in diversity, persuasiveness, and overall quality.

    Source: §1 · p. 2

  • Takeaways & Limitations

    PTCG provides a framework for generating multiple distinct counterarguments that combine persona-based perspectives with stepwise reasoning.

    Source: §1 · p. 2, §6 · p. 8

    The framework uses text-based personas derived from clustering and automated LLM planning without real-time human intervention, which can introduce potential LLM biases.

    Source: Limitations · p. 9

Abstract

from arXiv · show

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.

1 Introduction

The introduction identifies limited perspective diversity and persuasiveness as central gaps in counterargument generation. PTCG combines persona conditioning with stepwise plan generation and pruning, and the reported evaluations show improvements over baselines.

1 Introduction

  • Existing approaches often produce narrow, single-strategy counterarguments, while generated arguments may lack value-based reasoning and scenario-driven perspectives needed for persuasiveness.The cited work includes premise attacks, conclusion refutations, and logical-flaw identification.Source: §1 · p. 1
  • PTCG estimates the original author’s persona, selects similar and contrasting speaker personas, and combines them with Tree-of-Thoughts-inspired generation, evaluation, and pruning of candidate plans.The most promising plans are expanded into counterarguments grounded in different personas.Source: §1 · pp. 1–2
  • Across LLM-judge, classifier-based, and human evaluations, PTCG consistently outperforms baselines in diversity, persuasiveness, and overall quality.Experiments use multiple LLMs and ChangeMyView discussion threads.Source: §1 · p. 2

2 Related Work

Related work moves from rule-based and neural argument generation to counterargument strategies, perspective-taking, and audience-sensitive persuasion. PTCG distinguishes itself by producing multiple persona-specific counterarguments rather than a single synthesis.

2.1 Argument Generation

  • Early argument generation used symbolic pipelines and hand-crafted rhetorical patterns, while neural approaches added retrieved evidence to ground arguments in factual content.The cited examples are Sato et al. (2015), Wachsmuth et al. (2018), and Hua and Wang (2018).Source: §2.1 · p. 2

2.3 Perspective-Taking

  • Psychological work on perspective-taking and narrative transportation motivates generating multiple counterarguments that simulate meaningful disagreement and incorporate diverse viewpoints.The cited studies associate perspective-taking with empathy and altruistic motivation, and narrative transportation with attitude influence.Source: §2.3 · pp. 2–3

2.4 Diverse-Audience Persuasion

3 Persona-guided Tree-based Counterargument Generation

The task is to generate a set of persuasive counterarguments that challenge an argument from distinct perspectives. PTCG selects persona viewpoints and uses a stepwise generation-and-selection process to produce three outputs.

3.1 Task Description

  • The task takes an argument comprising a claim and supporting premises and returns counterarguments that each present a distinct perspective challenging it.Source: §3.1 · p. 3

3.2 Perspective Taking: Persona Selection

  • PTCG uses personas as proxies for viewpoints, clustering about 50,000 personas and selecting speakers from clusters at different distances to represent varied perspectives.Personas are embedded, reduced to 50 dimensions, and grouped into 39 clusters; inter-cluster distances guide speaker selection.Source: §3.2 · p. 3

3.3 Persona-guided Tree-based Counterargument Generation (PTCG) Framework

  • PTCG estimates the original poster’s beliefs, values, and worldview, then selects same-, nearest-, and furthest-cluster speaker personas to guide generation.For each persona, the LLM generates three candidate plans and selects one by voting; it then generates three candidate counterarguments and selects one, yielding three final outputs. Figure 1 summarizes the persona-conditioned, tree-based pruning process.Source: §3.3 · pp. 3–4, Figure 1 · p. 4

4 Experiments

The experiments use ChangeMyView posts and compare PTCG with prior methods and representative language models. Evaluation combines judge-based assessments with classifier-based scores for targeted persuasiveness.

4.1 Dataset

  • The dataset contains 847 ChangeMyView posts, each paired with three delta-awarded comments treated as quality persuasive counterarguments.Posts were filtered from 72,999 collected posts spanning 2013–2023.Source: §4.1 · pp. 4–5

4.2 Baselines

  • Baselines include prior counterargument-generation methods and representative LLMs.Source: §4.2 · p. 5

Backbone Language Models

  • The backbone-model comparisons include LLaMA3.1-8B-Instruct, LLaMA3.2-1B-Instruct, and Qwen3-8B and Qwen3-14B in non-thinking mode.Source: Backbone Language Models · p. 5

Counterargument Generation Methods

4.3 Evaluation Metrics

  • Evaluation uses LLM-as-a-Judge for diversity and general and targeted persuasiveness, plus classifier-based scores for targeted persuasiveness.General persuasiveness uses the post title; targeted persuasiveness uses the title and body, and scores are averaged across outputs.Source: §4.3 · p. 5

5 Results and Analysis

Across rating-scale, classifier-based, and pairwise evaluations, PTCG is reported to improve counterargument persuasiveness and diversity. Qualitative examples and ablations characterize its reasoning patterns and the trade-offs between persona grounding and tree search.

5.1 Rating-scale Evaluation

  • PTCG consistently outperforms same-backbone baselines in persuasiveness and perspective diversity on rating-scale evaluation.Chain-of-Thought prompting is a strong general baseline but weaker in targeted persuasiveness; PTCG surpasses Vanilla while improving diversity.Source: §5.1 · pp. 5–6, Table 1 · p. 6

5.2 Classifier-based Evaluation

  • Delta-classifier results favor PTCG across backbones, with its highest Max score reaching 0.90 on LLaMA3.1-8B despite a slightly lower Avg than Vanilla.Source: Table 2 · p. 6, §5.2 · p. 6

5.3 Pairwise Evaluation

  • Human and LLM pairwise evaluations favor PTCG over baselines in diversity and persuasiveness, though LLM comparisons have relatively high tie rates.Human ratings used 95 inputs and five evaluators; LLM comparisons tested both presentation orders.Source: Figure 2 · p. 7, §5.3 · p. 7

5.4 Qualitative Analysis

  • Examples show PTCG replacing abstract or single-claim rebuttals with practical contexts and persona-driven reframings.Persona distances yield internal-contradiction critiques, contextual nuances, or broader reframings; examples include healthcare trust and local-business impacts.Source: §5.4 · pp. 7–8

5.5 Ablation Study

  • PTCG’s combined persona grounding and tree search scores highest across metrics, recovering diversity while retaining general and targeted persuasiveness.Persona grounding alone boosts diversity but can reduce targeted persuasiveness; tree-based generation contributes most to persuasiveness but sharply reduces diversity.Source: Table 3 · p. 8, §5.5 · p. 8

6 Conclusion

The conclusion presents PTCG as a persona-grounded, tree-based approach for generating multiple distinct and persuasive counterarguments. Across human, LLM-based, and classifier-based evaluations, the authors report improved diversity and persuasiveness.

  • PTCG combines persona grounding with stepwise tree generation and pruning, and evaluations report improvements in counterargument diversity and persuasiveness.The authors frame structured reasoning and persona-driven perspective-taking as a promising direction for systems supporting critical thinking, balanced debates, and informed decision-making.Source: §6 · p. 8

Ethical Considerations

The study uses the publicly available Reddit ChangeMyView dataset and frames its work as academic exploration of perspective diversity rather than promotion of adversarial persuasion. It also notes limits to generalizability and the need for responsible-use safeguards.

  • The study uses publicly available Reddit ChangeMyView data and limits its stated aim to academic exploration of perspective diversity amid misuse concerns.The authors identify manipulative or coercive use of persuasive counterarguments as an ethical risk.Source: Ethical Considerations · p. 9
  • English-based Reddit data may introduce demographic bias and limit generalization to other languages or cultural rhetorical traditions.The authors call for future work on safeguards and responsible-use practices for deployment.Source: Ethical Considerations · p. 9

Limitations

PTCG is limited to single-opinion counterarguments, text-based clustered personas, and automated LLM planning without real-time human intervention. The authors identify multiround and multi-party debate, richer user signals, and human feedback as directions for future work.

Limitations

  • The framework generates counterarguments for a single opinion, leaving multiround and multi-party debates as future extensions.The authors describe multiround debate as enabling interactive exchanges in which counterarguments can be refined over multiple turns.Source: Limitations · p. 9
  • PTCG uses text-based personas derived from clustering and automated LLM planning without real-time human intervention, which can introduce potential LLM biases.The authors suggest richer user signals, including value-based attributes, and human feedback such as RLHF as possible improvements.Source: Limitations · p. 9

A1 LLM Usage

GPT-4o supported manuscript preparation, including identifying relevant prior work and improving the draft’s clarity and readability.

A1 LLM Usage

  • GPT-4o was used to support the manuscript preparation process.The stated use was auxiliary scholarly support.Source: A1 LLM Usage · p. 13
  • The model assisted in identifying relevant prior work.Source: A1 LLM Usage · p. 13
  • The model helped refine clarity and readability, including grammar and stylistic inconsistencies.The authors describe this use as limited to auxiliary scholarly support.Source: A1 LLM Usage · p. 13

A2 Persona Cluster Examples

The appendix illustrates persona-cluster organization with examples from three clusters, which are described as semantically coherent.

A2 Persona Cluster Examples

Sports and Physical Education

This supplementary section documents prompts and evaluation procedures for generating and assessing multiple counterarguments, alongside analyses of persona estimation, counterargument quality, persona distance, cross-backbone robustness, computational costs, and generation limitations.

Sports and Physical Education; Finance and Marketing

  • The supplementary persona examples span sports and physical education, finance and marketing, and historical research, including teachers, sports scientists, analysts, marketers, and historians.The examples include curriculum-oriented sports roles, investment and algorithmic-trading interests, marketing roles, and local or architectural historians.Source: Sports and Physical Education · p. 13, Finance and Marketing · p. 13

A3 Qualitative Analysis of Estimated Personas

  • In a qualitative review of 100 post-persona pairs, estimated personas captured explicit attributes and viewpoints, synthesized contextual cues, and could include plausible inferred details without needing to reconstruct real-world identity.Such persona signals were described as useful for planning and generating contextually rich, relevant counterarguments.Source: A3 Qualitative Analysis of Estimated… · pp. 13–14

A6 Quality Scores Across Backbone Models; A8 Ablation Study on Quality Scores

A7 Impact of Persona Distance on Persuasiveness and Diversity

  • Same and Nearest personas achieved targeted persuasiveness scores of 7.93 and 7.98, versus 7.86 for Furthest, while overall argument quality remained stable.The results describe a trade-off: closer personas support direct persuasion, whereas distant personas broaden perspective diversity.Source: Table A2 · p. 15, A7 Impact of Persona Distance on… · p. 15

A4 Implementation Details; A5 LLM-based Evaluation: Quality; A9 Human Evaluation

A10 Additional LLM-based Evaluation Results

A11 Error Analysis and Future Directions

  • Error analysis identified occasional persona-topic mismatches that produce unnatural reasoning and generic rather than persona-distinctive style.The authors suggest persona-topic filtering and prompt constraints on tone, style, and word choice.Source: A11 Error Analysis and Future Directions · p. 17

A12 Scalability and Efficiency Analysis

  • With n=3, PTCG took nearly twice the baseline’s total time but had lower per-response latency; persona-only latency rose about 12%, versus about 111% for tree-based generation.The analysis attributes most added computation to tree-based generation and evaluation rather than persona integration.Source: A12 Scalability and Efficiency Analysis · pp. 17–18, Table A4 · p. 18, Table A5 · p. 18
  • The appendix shows a vanilla-LLM generation prompt and evaluation prompts for general and targeted persuasiveness, perspective diversity, and quality.General persuasiveness is assessed against the original post’s title, while targeted persuasiveness uses both its title and body; quality covers appropriateness, clarity, grammaticality, and relevance.Source: Figure A7 · p. 26, Figure A8 · p. 27, Figure A9 · p. 28
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