New method uses constraints to boost LLM enterprise data mapping

Researchers have developed a constraint-guided mapping (CGM) method to improve the accuracy and efficiency of large language models (LLMs) in enterprise data alignment tasks. This neuro-symbolic approach uses admissibility constraints derived from schema metadata to restrict candidate generation, followed by neural ranking with LLM disambiguation. The method significantly reduces the search space and expert effort, outperforming LLM-only matching by providing operationally valid correspondences while maintaining semantic recall. AI

影响 Enhances LLM utility in enterprise data integration by improving accuracy and reducing manual effort.

排序理由 The cluster contains a research paper detailing a new methodology for LLM data mapping. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• alphaXiv
• CatalyzeX
• Connected Papers
• Constraint-Guided Mapping
• DagsHub
• Gotit.pub
• Hugging Face
• large-language models
• Litmaps
• ScienceCast
• scite Smart Citations
• Valentine

• 论文
• 产品

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →