个性化说服的生成框架：推断因果、反事实与潜在知识
Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge
AI总结：
本文提出基于因果发现和反事实推断的个性化说服对话生成框架，将心理构造等视为潜在因子并有效估计，通过优化系统响应策略显著提升了说服效果。
摘要：
我们假设最优的系统响应源于基于因果和反事实知识的自适应策略。反事实推断允许我们创建假设场景，以检验替代系统响应的效果。我们通过因果发现增强了这一过程，该方法能够识别由控制系统行为的潜在因果结构所提供的策略。此外，我们将可能影响用户与系统交互的心理构造和不可观测噪声视为潜在因子。我们表明这些因子可以被有效估计。我们采用因果发现来识别用户与系统话语之间的策略级因果关系，从而指导个性化反事实对话的生成。我们将用户话语策略建模为因果因子，使系统策略能够被视为反事实动作。此外，我们基于反事实数据优化了选择系统响应的策略。我们使用一个关于社会公益的真实世界数据集所得的结果表明，说服性系统结果有显著改善，累积奖励的增加验证了因果发现在指导个性化反事实推断和优化说服性对话系统的对话策略方面的有效性。
英文摘要：
We hypothesize that optimal system responses emerge from adaptive strategies grounded in causal and counterfactual knowledge. Counterfactual inference allows us to create hypothetical scenarios to examine the effects of alternative system responses. We enhance this process through causal discovery, which identifies the strategies informed by the underlying causal structure that govern system behaviors. Moreover, we consider the psychological constructs and unobservable noises that might be influencing user-system interactions as latent factors. We show that these factors can be effectively estimated. We employ causal discovery to identify strategy-level causal relationships among user and system utterances, guiding the generation of personalized counterfactual dialogues. We model the user utterance strategies as causal factors, enabling system strategies to be treated as counterfactual actions. Furthermore, we optimize policies for selecting system responses based on counterfactual data. Our results using a real-world dataset on social good demonstrate significant improvements in persuasive system outcomes, with increased cumulative rewards validating the efficacy of causal discovery in guiding personalized counterfactual inference and optimizing dialogue policies for a persuasive dialogue system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
