让AI写说服性论点时懂观众心理，还能选对策略
ARGUS: Theory-of-Mind Guided Argument Generation with Strategy-Aware Planning and Knowledge Grounding
- 用心理理论建模观众信念与价值观，指导论证生成
- 规划阶段自动分配逻辑、情感、可信度等修辞功能
- 适合需要高说服力的对话系统与内容生成场景
说服性论点生成需建模受众信念、修辞策略与事实依据。现有方法多忽视受众差异，且缺乏策略选择机制。为此，我们提出Argus，一种基于智能体的框架，将经典修辞学转化为可操作流程。核心是一个理论心智（ToM）推理器，显式构建受众信念与价值观的双重心理模型，以指导后续决策。该模型驱动一个组件感知的规划器，将论点分解为子话题，分配细粒度修辞功能（逻辑、情感、可信度），并在规划阶段触发策略导向的证据检索。最后，一个优化模块迭代识别并修复多维度弱点，避免质量下降。我们在三个多样化基准上评估，采用自动化成对Elo与大模型评判指标。结果表明，Argus在多种主干模型下均持续优于强基线，取得最高排名与总分。针对性模拟实验进一步验证其有效改变顽固受众立场的能力。
原文摘要 · Abstract (English)
Persuasive argument generation requires modeling audience beliefs, rhetorical strategies, and factual grounding. Despite recent advancements, existing methods remain largely audience-agnostic and fail to integrate strategy selection to improve persuasiveness. To bridge this gap, we propose Argus, an agent-based framework that operationalizes classical rhetoric for persuasive writing. At its core, a Theory-of-Mind (ToM) Reasoner constructs an explicit dual mental model of the audience's beliefs and values to guide downstream decisions. This representation conditions a component-aware planner that decomposes the argument into subtopics, assigns fine-grained rhetorical functions (logos, pathos, ethos), and triggers strategy-guided evidence retrieval at planning time. Finally, a refinement module iteratively targets and resolves multi-dimensional weaknesses without quality regression. We evaluate Argus across three diverse benchmarks using both automated pairwise Elo and LLM-as-judge metrics. Results show that Argus consistently outperforms strong baselines across multiple backbone models, achieving top rankings and the highest overall scores. Targeted simulation experiments further validate its effectiveness in shifting resistant audience stances.
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