AI Research
Logic-Guided Data Extraction with Answer Set Programming and Large Language Models
Bridging neuro-symbolic reasoning and LLMs to solve complex data extraction consistency challenges
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. This paper proposes a...
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ArXiv CS.AI
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ArXiv CS.AI
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Integrating Answer Set Programming with large language models offers a robust framework for ensuring logical consistency in automated data extraction. This hybrid approach significantly reduces hallucination risks and computational costs by delegating complex reasoning to specialized solvers.
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