ID-GMLM: Intelligent Decision-Making with Integrated Graph Models and Large Language Models

Multi-criteria decision making (MCDM) and preference learning (PL) are crucial subfields of intelligent decision-making, both aiming to aid decision-makers (DMs) in selecting, classifying, or ranking alternatives. While MCDM and PL can complement each other to some extent, existing approaches combining MCDM and PL often struggle with large data volumes and complex relational information. To address this, we propose a novel approach called ID-GMLM that integrates graph models and large language models (LLMs) for intelligent decision-making. It reformulates decision-making as a high-parallelism ranking function in the graph domain, using graph neural networks (GNNs) to learn and understand complex relationships between alternatives or criteria, and LLMs to parse and quantify the preferences of DMs. ID-GMLM features a multi-task learning framework that optimizes the primary task of predicting alternative rankings while modeling criterion interactions through the auxiliary task. Additionally, ID-GMLM incorporates a parameter tuning network based on criterion weights and an attention network, allowing the model to adaptively adjust to the context of the current task and the evolving preferences of DMs. Experiments on benchmark datasets demonstrate that ID-GMLM achieves significant performance improvements, inheriting the interpretability and intuitive appeal of MCDM while leveraging the computational efficiency and high accuracy of PL.