Enhancing LLMs in Predictive Political QA with Semi-Structured Data

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.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)

arXiv:2608.21218 [cs.AI]

(or arXiv:2608.21218v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.21218 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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