Model Hemorrhage and the Robustness Limits of Large Language Models
This work addresses the challenge of maintaining LLM performance during deployment modifications for researchers and practitioners, though it is incremental in proposing specific strategies within an existing framework.
The paper tackles the problem of performance degradation in large language models (LLMs) when modified for deployment, defining it as 'model hemorrhage' and identifying key vulnerability patterns like attention disruption and information loss. It proposes mitigation strategies such as gradient-aware pruning and dynamic quantization scaling, establishing foundational metrics for evaluating model stability during adaptation.
Large language models (LLMs) demonstrate strong performance across natural language processing tasks, yet undergo significant performance degradation when modified for deployment through quantization, pruning, or decoding strategy adjustments. We define this phenomenon as model hemorrhage - performance decline caused by parameter alterations and architectural changes. Through systematic analysis of various LLM frameworks, we identify key vulnerability patterns: layer expansion frequently disrupts attention mechanisms, compression techniques induce information loss cascades, and decoding adjustments amplify prediction divergences. Our investigation reveals transformer architectures exhibit inherent robustness thresholds that determine hemorrhage severity across modification types. We propose three mitigation strategies: gradient-aware pruning preserves critical weight pathways, dynamic quantization scaling maintains activation integrity, and decoding calibration aligns generation trajectories with original model distributions. This work establishes foundational metrics for evaluating model stability during adaptation, providing practical guidelines for maintaining performance while enabling efficient LLM deployment. Our findings advance understanding of neural network resilience under architectural transformations, particularly for large-scale language models.