arXiv:2512.10999v3 Announce Type: replace Abstract: Knowledge Base Question Answering (KBQA) challenges models to bridge the gap between natural language and strict knowledge graph schemas by generating executable logical forms. While Large Language Models (LLMs) have advanced this field, current approaches often struggle with a dichotomy of failure: they either generate hallucinated queries without verifying schema existence or exhibit rigid, template-based reasoning that mimics synthesized traces without true comprehension of the environment. To address these limitations, we present \textbf{
The increasing sophistication and widespread deployment of LLMs mandate more robust and reliable methods for knowledge base interaction, pushing research toward solving persistent hallucination and rigidity issues.
Improved KBQA directly enhances the utility and trustworthiness of LLMs in complex, data-driven applications, making them more effective for enterprise and specialized domains requiring factual accuracy.
This research introduces concrete advancements in LLM reasoning for factual retrieval, moving beyond simple pattern matching to a more verifiable and schema-aware query generation process.
- · AI developers
- · Enterprise AI users
- · Knowledge management platforms
- · Data scientists
- · Companies relying on unreliable LLM output
- · Developers implementing naive KBQA solutions
LLMs become more reliable and less prone to hallucination when performing factual queries.
This improved reliability accelerates the deployment of LLMs into critical decision-support systems and automated workflows.
Increased trust in LLM-driven systems could lead to a deeper integration of AI agents into white-collar professions, augmenting or replacing tasks requiring extensive knowledge retrieval.
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