LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection

• URL: http://arxiv.org/abs/2505.03793v3
• Date: Sat, 31 May 2025 16:36:42 GMT
• Title: LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
• Authors: Xinyue Zeng, Haohui Wang, Junhong Lin, Jun Wu, Tyler Cody, Dawei Zhou,
• Abstract summary: Open-sourced Large Language Models (LLMs) and diverse downstream tasks require efficient model selection.<n>We propose a novel theoretical framework that provides a proper lens to assess the generalization capabilities of LLMs.<n>In particular, we first derive a PAC-Bayesian Generalization Bound that unveils fine-tuning dynamics of LLMs.<n>We then introduce LENSLLM, a Neural Tangent Kernel (NTK)-based Rectified Scaling Model that enables accurate performance predictions.
• Score: 11.353302879735862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of open-sourced Large Language Models (LLMs) and diverse downstream tasks necessitates efficient model selection, given the impracticality of fine-tuning all candidates due to computational constraints. Despite the recent advances in LLM selection, a fundamental research question largely remains nascent: how can we model the dynamic behaviors of LLMs during fine-tuning, thereby enhancing our understanding of their generalization performance across diverse downstream tasks? In this work, we propose a novel theoretical framework that provides a proper lens to assess the generalization capabilities of LLMs, thereby enabling accurate and efficient LLM selection for downstream applications. In particular, we first derive a PAC-Bayesian Generalization Bound that unveils fine-tuning dynamics of LLMs and then introduce LENSLLM, a Neural Tangent Kernel (NTK)-based Rectified Scaling Model that enables accurate performance predictions across diverse tasks while maintaining computational efficiency. Extensive empirical results on 3 large-scale benchmarks demonstrate that our model achieves up to 91.1% accuracy and reduces up to 88.5% computational cost in LLM selection, outperforming 5 state-of-the-art methods. We open-source our proposed LENSLLM model and corresponding results at LensLLM.io.

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