Enhancing LLMs in Predictive Political QA with Semi-Structured Data

2026-08-21

AI Summary

研究通过PSL框架，利用半结构化数据中的立场和结构信号增强LLM在预测性政治问答中的表现，提高了对政治行为预测的准确性。

Abstract

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.

Problem

• Predictive Political QA
• Semi-Structured Data
• External Resources
• Political Reasoning

Innovation

• Dual-View Framework
• Stance Signals
• Structure-Aware Representations
• Semi-Structured Data