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Weight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs
Understanding which parameters are influential in Large Language Models (LLMs) is central to improving their efficiency, reliability, and interpretability. We introduce Weight-Adjusted Gradients (WAG), a simple yet effective approach for estimating parameter importance that explicitly captures the i
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