Integrating the Analytic Hierarchy Process with Large Language Models for Transparent Multi-Criteria Decision-Making

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.

Artificial Intelligence (cs.AI)

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)

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