Enhancing LLMs in Predictive Political QA with Semi-Structured Data
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 prefe
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- PossiblePossibly related (embedding) · 46%Large Language Models: Qwen3 Offers AI Models For Deeper Reasoning And Faster Responses - Trend Hunter →
- LinkedLinked via arxiv author · 85%Yinan Liu →
“Enhancing LLMs in Predictive Political QA with Semi-Structured Data”
- LinkedLinked via arxiv author · 85%Zihan Zhou →
“Enhancing LLMs in Predictive Political QA with Semi-Structured Data”
- LinkedLinked via arxiv author · 85%Zichun Jin →
“Enhancing LLMs in Predictive Political QA with Semi-Structured Data”
- LinkedLinked via arxiv author · 85%Xinyu Wang →
“Enhancing LLMs in Predictive Political QA with Semi-Structured Data”
- LinkedLinked via arxiv author · 85%Bin Wang →
“Enhancing LLMs in Predictive Political QA with Semi-Structured Data”
- LinkedLinked via arxiv author · 85%Xiaochun Yang →
“Enhancing LLMs in Predictive Political QA with Semi-Structured Data”
- FuzzyOverlapping authors or contributors · 62%keras-team/keras →
“Shared author/contributor keys: jin”