用结构化规划生成更真实的信用卡网络讨论。
CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation
- 通过规划器控制回复结构与对话风格，提升生成真实度。
- 相比基线模型，生成内容在语义和结构上更接近真实讨论。
- 适合研究消费者金融行为或生成高质量模拟数据的学者。
在线信用卡讨论为研究消费者如何交流金融产品提供了自然场景。模拟这些讨论不仅需生成单条评论，还需匹配真实用户表达方式与互动模式。我们提出CARD框架，用于生成逼真的信用卡讨论线程。给定一个信用卡帖子及其对应的真实线程，CARD采用非逐字的指导，控制回复结构、评论功能、立场、语气及对话多样性。规划器组织这些约束，写作者生成讨论，校准循环则迭代更新评论分布，以缩小生成线程与真实线程的分布差异。我们在真实Reddit信用卡讨论上评估CARD，使用词汇、语义、行为和结构指标。CARD在多个大语言模型上均优于基线，且各项指标的效应量和分布距离更小。结果表明，结构化规划与定向修正能有效提升模拟讨论的真实性。
原文摘要 · Abstract (English)
Online credit card discussions provide a natural setting for studying how consumers communicate about financial products. Simulating these discussions requires more than just generating individual comments, the generated threads should also match how real users express themselves and interact with others. We introduce CARD, a framework for generating realistic credit card discussion threads. Given a credit card post and its matched real thread, CARD uses non-verbatim guidance on reply structure, comment function, stance, tone, and conversational variation. A planner organizes these controls, a writer generates the discussion, and a calibration loop updates comments' populations that contribute to differences between the generated and real thread distributions. We evaluate CARD on real Reddit credit card discussions using lexical, semantic, behavioral, and structural metrics. CARD matches the distributions of real credit card discussions better than simulation baselines across multiple LLMs and also demonstrates smaller effect sizes and distribution distances across metrics. These results show that structured planning and targeted revision can generate the realism of simulated credit card discussions.
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