The Super Weight in Large Language Models
Keywords: natural language processing
TL;DR: We discover and study "super weights" in LLM, which are very few in numbers yet crucial to model quality.
Abstract: Recent works have shown a surprising result: a small fraction of Large Language Model (LLM) parameter outliers are disproportionately important to the quality of the model. LLMs contain billions of parameters, so these small fractions, such as 0.01%, translate to hundreds of thousands of parameters. In this work, we present an even more surprising finding: pruning as few as a single parameter can destroy an LLM’s ability to generate text—resulting in an increase in perplexity by three orders of magnitude and reducing zero-shot accuracy to guessing. We propose a data-free method for identifying such parameters, termed super weights, using a single forward pass through the model. Additionally, we find that these super weights induce correspondingly rare and large activation outliers, termed super activations. When preserved with high precision, super activations can enhance simple round-to-nearest quantization, making it competitive with state-of-the-art methods. For weight quantization, we similarly find that by preserving the super weight and clipping other weight outliers, round-to-nearest quantization can scale to much larger block sizes than previously considered. To facilitate further research into super weights, we provide an index of super weight coordinates for common, openly available LLMs.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 310
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