MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization

• URL: http://arxiv.org/abs/2509.09387v3
• Date: Tue, 07 Oct 2025 13:08:21 GMT
• Title: MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
• Authors: Mohamed Bal-Ghaoui, Mohammed Tiouti,
• Abstract summary: We propose MetaLLMiX, a framework combining meta-learning, explainable AI, and efficient LLM reasoning.<n>We show that MetaLLMiX achieves competitive or superior performance to traditional HPO methods while drastically reducing computational cost.<n>Our local deployment outperforms prior API-based approaches, achieving optimal results on 5 of 8 tasks, response time reductions of 99.6-99.9%, and the fastest training times on 6 datasets (2.4-15.7x faster)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective model and hyperparameter selection remains a major challenge in deep learning, often requiring extensive expertise and computation. While AutoML and large language models (LLMs) promise automation, current LLM-based approaches rely on trial and error and expensive APIs, which provide limited interpretability and generalizability. We propose MetaLLMiX, a zero-shot hyperparameter optimization framework combining meta-learning, explainable AI, and efficient LLM reasoning. By leveraging historical experiment outcomes with SHAP explanations, MetaLLMiX recommends optimal hyperparameters and pretrained models without additional trials. We further employ an LLM-as-judge evaluation to control output format, accuracy, and completeness. Experiments on eight medical imaging datasets using nine open-source lightweight LLMs show that MetaLLMiX achieves competitive or superior performance to traditional HPO methods while drastically reducing computational cost. Our local deployment outperforms prior API-based approaches, achieving optimal results on 5 of 8 tasks, response time reductions of 99.6-99.9%, and the fastest training times on 6 datasets (2.4-15.7x faster), maintaining accuracy within 1-5% of best-performing baselines.

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