RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind
Zhitao He, Zongwei Lyu, Yi R Fung

TL;DR
RebuttalAgent introduces a novel AI framework grounded in Theory of Mind for strategic academic rebuttal, utilizing a large dataset and reinforcement learning to improve persuasion effectiveness beyond surface-level linguistic approaches.
Contribution
This paper presents the first ToM-based framework for academic rebuttal, including a new dataset, a two-stage training process, and a specialized evaluator, advancing strategic persuasion in AI.
Findings
RebuttalAgent outperforms baseline models by 18.3% on automated metrics.
The framework surpasses proprietary models in automated and human evaluations.
Rebuttal-RM achieves higher scoring consistency with human preferences than GPT-4.
Abstract
Although artificial intelligence (AI) has become deeply integrated into various stages of the research workflow and achieved remarkable advancements, academic rebuttal remains a significant and underexplored challenge. This is because rebuttal is a complex process of strategic communication under severe information asymmetry rather than a simple technical debate. Consequently, current approaches struggle as they largely imitate surface-level linguistics, missing the essential element of perspective-taking required for effective persuasion. In this paper, we introduce RebuttalAgent, the first framework to ground academic rebuttal in Theory of Mind (ToM), operationalized through a ToM-Strategy-Response (TSR) framework that models reviewer mental state, formulates persuasion strategy, and generates evidence-based response. To train our agent, we construct RebuttalBench, a large-scale…
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Taxonomy
TopicsEthics and Social Impacts of AI · Multimodal Machine Learning Applications · Explainable Artificial Intelligence (XAI)
