An agentic framework that leverages Large Language Models (LLMs) for generating UDF-centric queries from natural language task descriptions in the medical domain is presented, demonstrating that structured tool orchestration with verification loops substantially improves generation quality.
The findings suggest that governed pilot deployment for outpatient schedule monitoring may be feasible under controlled institutional conditions, while indicating that the main remaining barriers are semantic rather than purely syntactic, specifically ambiguity handling, institution-specific operational language, and f...
Isaac Daroch, Matías Rojas Cabrera, Rodrigo Muñoz Andrade et al.· Big Data and Cognitive Compu...· 0 citations
Six retrieval configurations that vary along two axes: backend (a property graph database, a relational database and a dense vector index) and interface design (curated domain-specific tool calls, model-generated queries, full-text search, and single-shot dense retrieval) are compared.
Leonidas Anagnou, Andreas Vezakis, Ioannis A Vezakis et al.· Future Internet· 0 citations
The empirical findings indicate that factorizing ML-aware SQL generation into four distinct stages—query routing, structured intent extraction, model or function selection, and template-guided SQL synthesis—enhances semantic controllability and token efficiency when formulating predictive natural language queries over...
The results support a trade-off interpretation rather than a universal ranking: additional structural constraints changed failure modes and efficiency, but did not monotonically improve correctness or solve ambiguity and multi-turn state consistency.
It is shown that when domain vocabulary and semantics are captured in a well-designed Web Ontology Language (OWL) ontology, Large Language Models (LLMs) can generate accurate structured queries zero-shot, without task-specific fine-tuning, retrieval augmentation, or multi-agent orchestration.
This paper studies the VKG-QA task, which enables users to interact with the VKGs through a natural language (NL) interface by translating their questions into SPARQL queries, and pro-poses NaVQA (Navigation-based VKG Question Answering), a framework leveraging Large Language Models.
Guohui Xiao, Haohan Xue, Lin Ren et al.· Proceedings of the Thirty-Fi...· 0 citations
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