Logic-Guided Data Extraction with Answer Set Programming and Large Language Models
This paper proposes a logic-guided data extraction framework combining LLM-based extraction with Answer Set Programming (ASP). The LLM produces candidate facts, whereas ASP performs validation, inference, consistency checking, and control. Unlike existing pipelines that query the LLM independently for all target predicates, the proposed approach uses ASP reasoning to identify which predicates are logically admissible at each stage and to guide extraction queries.
- ▪This paper proposes a logic-guided data extraction framework combining LLM-based extraction with Answer Set Programming (ASP).
- ▪The LLM produces candidate facts, whereas ASP performs validation, inference, consistency checking, and control.
- ▪Unlike existing pipelines that query the LLM independently for all target predicates, the proposed approach uses ASP reasoning to identify which predicates are logically admissible at each stage and to guide extraction queries.
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Computer Science > Artificial Intelligence arXiv:2607.19365 (cs) [Submitted on 8 Jun 2026] Title:Logic-Guided Data Extraction with Answer Set Programming and Large Language Models Authors:Mario Alviano, Lorenzo Grillo, Nicola Leone, Fabrizio Lo Scudo View a PDF of the paper titled Logic-Guided Data Extraction with Answer Set Programming and Large Language Models, by Mario Alviano and 2 other authors View PDF HTML (experimental) Abstract:When Large Language Models (LLMs) are used for semantic data extraction from unstructured text, producing candidate relational facts from natural language, they may remain unreliable for tasks requiring complex combinatorial reasoning and global consistency.
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