One for All: A General Framework of LLMs-based Multi-Criteria Decision Making on Human Expert Level

• URL: http://arxiv.org/abs/2502.15778v1
• Date: Mon, 17 Feb 2025 06:47:20 GMT
• Title: One for All: A General Framework of LLMs-based Multi-Criteria Decision Making on Human Expert Level
• Authors: Hui Wang, Fafa Zhang, Chaoxu Mu,
• Abstract summary: We propose an evaluation framework to automatically deal with general complex MCDM problems.<n>Within the framework, we assess the performance of various typical open-source models, as well as commercial models such as Claude and ChatGPT.<n>The experimental results show that the accuracy rates for different applications improve significantly to around 95%, and the performance difference is trivial between different models.
• Score: 7.755152930120769
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-Criteria Decision Making~(MCDM) is widely applied in various fields, using quantitative and qualitative analyses of multiple levels and attributes to support decision makers in making scientific and rational decisions in complex scenarios. However, traditional MCDM methods face bottlenecks in high-dimensional problems. Given the fact that Large Language Models~(LLMs) achieve impressive performance in various complex tasks, but limited work evaluates LLMs in specific MCDM problems with the help of human domain experts, we further explore the capability of LLMs by proposing an LLM-based evaluation framework to automatically deal with general complex MCDM problems. Within the framework, we assess the performance of various typical open-source models, as well as commercial models such as Claude and ChatGPT, on 3 important applications, these models can only achieve around 60\% accuracy rate compared to the evaluation ground truth. Upon incorporation of Chain-of-Thought or few-shot prompting, the accuracy rates rise to around 70\%, and highly depend on the model. In order to further improve the performance, a LoRA-based fine-tuning technique is employed. The experimental results show that the accuracy rates for different applications improve significantly to around 95\%, and the performance difference is trivial between different models, indicating that LoRA-based fine-tuned LLMs exhibit significant and stable advantages in addressing MCDM tasks and can provide human-expert-level solutions to a wide range of MCDM challenges.

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