将层次分析法与大型语言模型相结合以实现透明的多准则决策

Computer Science > Artificial Intelligence arXiv:2609.16779 (cs) [Submitted on 15 Sep 2026] Title:Integrating the Analytic Hierarchy Process with Large Language Models for Transparent Multi-Criteria Decision-Making Authors:Han Zhiguang, Farah Benamara (IRIT-MELODI, UT3, IPAL), Pascale Zaraté (IRIT, UT Capitole, IRIT-ADRIA) View a PDF of the paper titled Integrating the Analytic Hierarchy Process with Large Language Models for Transparent Multi-Criteria Decision-Making, by Han Zhiguang and 6 other authors View PDF Abstract:LLMs are increasingly employed in a wide range of decision-making tasks. However, the opacity of their internal reasoning makes it difficult to validate or interpret their outputs, and the need for interpretability becomes especially critical in high-stakes settings. This study examines the decision-making capabilities of LLMs through the Analytic Hierarchy Process (AHP), a classical and widely used multicriteria decision-making framework. We construct a new annotated benchmark based on AHP and propose the first end-to-end approach that enables LLMs to perform the complete AHP workflow. Experiments in real-world decision problems in the legal and higher-education ranking domains show that our method significantly improves alignment with expert judgments. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.16779 [cs.AI] (or arXiv:2609.16779v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.16779 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Pascale Zarate [view email] [via CCSD proxy] [v1] Tue, 15 Sep 2026 07:47:04 UTC (241 KB) Full-text links: Access Paper: View a PDF of the paper titled Integrating the Analytic Hierarchy Process with Large Language Models for Transparent Multi-Criteria Decision-Making, by Han Zhiguang and 6 other authors View PDF TeX Source view license Current browse context: cs.AI new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)