用政治人物立场和互动结构提升大模型预测投票行为能力
Enhancing LLMs in Predictive Political QA with Semi-Structured Data
- 从半结构化记录中提取立场与互动结构双信号
- 在三个真实数据集上显著优于基线方法
- 适合政治分析、政策预测等需要推理的场景
预测性政治问答（如预测政治人物如何投票）超越了事实查找。外部政治资源包含丰富历史证据，但通常不直接提供答案。现有大模型增强方法（如基于人物档案的模拟、知识图谱证据注入）虽提升政治推理能力，但多将外部资源视为知识型证据，忽略了预测相关的潜在信号。本文识别出两类互补信号：体现议题偏好的人物立场，以及反映政治人物间间接关联的高阶结构信号。提出PSL双视角框架，将半结构化政治记录转化为面向推理的证据。该框架在语义视图中提取问题相关人物记录中的立场信号，在向量视图中通过人物互动图学习结构感知的人物表征。在三个真实数据集和多个大模型上，PSL持续优于基线，消融实验验证了立场与结构信号的互补增益。
原文摘要 · Abstract (English)
Predictive political question answering (QA), such as predicting how a political actor will vote, goes beyond factual lookup. External political resources offer rich historical evidence, but rarely contain the answer itself. Existing LLM augmentation methods, including actor-profile-based simulation and knowledge graph evidence injection, improve political reasoning but largely treat external resources as knowledge-based evidence, leaving prediction-relevant signals under-modeled. We identify two complementary signals for predictive political QA: actor stances that capture issue-specific preferences, and high-order structure signals that capture indirect dependencies among political actors. We propose PSL, a dual-view framework that converts semi-structured political records into inference-oriented evidence for LLMs. PSL extracts stance signals from question-relevant actor records in a semantic view, and learns structure-aware actor representations from an actor interaction graph in a vector view. Across three real-world datasets and multiple LLMs, PSL consistently outperforms baselines, with ablations confirming the complementary gains of stance and structure signals.
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