利用半结构化数据增强大型语言模型（LLMs）在预测性政治问答（QA）中的表现
Enhancing LLMs in Predictive Political QA with Semi-Structured Data
- School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对预测性政治问答任务，提出双视图框架PSL，结合半结构化政治记录提取立场与结构信号，在三个真实数据集及多个LLM上均优于基线方法。
AI中文摘要：
预测性政治问答（QA），例如预测政治行为者将如何投票，超越了事实查询。外部政治资源提供了丰富的历史证据，但很少包含答案本身。现有的大型语言模型（LLM）增强方法，包括基于行为者档案的模拟和知识图谱证据注入，提升了政治推理能力，但大多将外部资源视为基于知识的证据，对预测相关信号的建模不足。我们确定了预测性政治问答的两种互补信号：捕捉特定问题偏好的行为者立场，以及捕捉政治行为者间间接依赖关系的高阶结构信号。我们提出了PSL，一个双视图框架，将半结构化政治记录转换为面向LLM的推理证据。PSL在语义视图中从与问题相关的行为者记录中提取立场信号，并在向量视图中从行为者交互图中学习感知结构的行为者表示。在三个真实世界数据集和多个LLM上，PSL始终优于基线方法，消融实验证实了立场和结构信号的互补增益。
英文摘要：
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.
