LensLLM

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告别盲选LLM!ICML 2025新研究解释大模型选择的「玄学」
机器之心· 2025-07-04 08:59
Core Viewpoint - The article introduces the LensLLM framework developed by Virginia Tech, which significantly enhances the efficiency of selecting large language models (LLMs) while reducing computational costs, thus addressing the challenges faced by researchers and developers in model selection [2][3][4]. Group 1: Introduction - The rapid advancement of LLMs has created a challenge in model selection, as traditional methods are resource-intensive and yield limited results [4]. Group 2: Theoretical Breakthrough of LensLLM - LensLLM is based on a novel PAC-Bayesian Generalization Bound, revealing unique dynamics in the relationship between test loss and training data size during LLM fine-tuning [6][10]. - The framework provides a first-principles explanation of the "phase transition" in LLM fine-tuning performance, indicating when data investment leads to significant performance improvements [12][16]. Group 3: LensLLM Framework - LensLLM incorporates Neural Tangent Kernel (NTK) to accurately capture the complex dynamics of transformer architectures during fine-tuning, establishing a precise relationship between model performance and data volume [15][16]. - The framework demonstrates impressive accuracy in curve fitting and test loss prediction across various benchmark datasets, outperforming traditional models [17][18]. Group 4: Performance and Cost Efficiency - LensLLM achieved a Pearson correlation coefficient of 85.8% and a relative accuracy of 91.1% on the Gigaword dataset, indicating its effectiveness in ranking models [21]. - The framework reduces computational costs by up to 88.5% compared to FullTuning, achieving superior performance with significantly lower FLOPs [23][25]. Group 5: Future Prospects - The research opens new avenues for LLM development and application, with potential expansions into multi-task scenarios and emerging model architectures like Mixture of Experts (MoE) [27][30]. - LensLLM is particularly suited for resource-constrained environments, accelerating model testing and deployment cycles while maximizing performance [31].
告别玄学选LLM!弗吉尼亚理工选型框架入选ICML 2025
量子位· 2025-06-18 04:58
Core Viewpoint - The article discusses the introduction of the LensLLM framework by researchers from Virginia Tech, which significantly improves model selection efficiency while reducing costs by nearly 90% [1][2]. Group 1: Model Selection Challenges - In the era of rapidly emerging large language models (LLMs), selecting the right model has become a major pain point for AI engineers and researchers [2][6]. - Existing methods rely heavily on experience and trial-and-error, making it difficult to balance cost and effectiveness [8][9]. - Incorrect model selection can lead to wasted GPU resources, slowed product iteration, and even project failures [7]. Group 2: LensLLM Framework - LensLLM aims to end the era of model selection based on intuition, providing a theoretical foundation derived from a new PAC-Bayes generalization bound [9]. - The framework reveals the nonlinear performance changes of LLMs during fine-tuning based on different data scales [9][10]. - It introduces a phase transition phenomenon in model performance, transitioning from "pre-power law" to "power law" as data volume increases [13][14]. Group 3: Performance Prediction and Cost Efficiency - LensLLM utilizes a neural tangent kernel (NTK) enhanced scaling law model to accurately predict model performance with minimal data fine-tuning [18][19]. - The framework demonstrates superior prediction accuracy compared to baseline methods, achieving RMSE errors as low as one-fifth of previous methods [22][24]. - It significantly reduces computational costs, with a maximum reduction of 88.5% compared to full tuning methods while maintaining a high selection accuracy of 91.1% [26][27]. Group 4: Future Applications - LensLLM is positioned not only as a model selection tool but also as a potential core component for model evaluation and management [28]. - Future explorations will include expanding LensLLM to multi-task environments and complex model structures, aiming to create a more universal intelligent model selection system [28].