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    <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.</abstract>
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%0 Conference Proceedings
%T Enhancing LLMs in Predictive Political QA with Semi-Structured Data
%A Liu, Yinan
%A Zhou, Zihan
%A Jin, Zichun
%A Wang, Xinyu
%A Wang, Bin
%A Yang, Xiaochun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-enhancing
%X 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.
%U https://aclanthology.org/2026.emnlp-main.414/
%P 9490-9503

Markdown (Informal)

[Enhancing LLMs in Predictive Political QA with Semi-Structured Data](https://aclanthology.org/2026.emnlp-main.414/) (Liu et al., EMNLP 2026)

• Enhancing LLMs in Predictive Political QA with Semi-Structured Data (Liu et al., EMNLP 2026)

ACL

• Yinan Liu, Zihan Zhou, Zichun Jin, Xinyu Wang, Bin Wang, and Xiaochun Yang. 2026. Enhancing LLMs in Predictive Political QA with Semi-Structured Data. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 9490–9503, Budapest, Hungary. Association for Computational Linguistics.