Papers by Jianqi Gao
Generating then Refining for Reliable Knowledge Base Question Answering (2026.acl-long)
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| Challenge: | Existing knowledge base question answering methods generate LFs that are non-executable due to semantic hallucination issue of large language models. |
| Approach: | They propose a "generate-verify-refine" framework for reliable LF generation . they propose ARI-KBQA to generate query paths based on hop-by-hop reasoning . |
| Outcome: | The proposed framework significantly improves model performance with a reduced search space . ARI-KBQA can generate LFs that are non-executable due to semantic hallucination issue . |
KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)
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| Challenge: | Existing methods for answering natural language questions are difficult to generate . lack of a logical form for complex graphs can negatively impact overall performance . |
| Approach: | They propose a generate-then-retrieve method that converts questions into structured LF queries . they propose to combine knowledge-aware question decomposition and progressive LF generation . |
| Outcome: | The proposed method achieves state-of-the-art (SOTA) performance on WebQuestionSP and ComplexWebQuestions benchmarks. |