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SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs
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depth pruningmodel compressionresidual networksfine-tuning
2608.25068
Builder Relevance
2h ago80%
Abstract
SHIFT-LLM introduces a training-free post-pruning correction framework that mitigates accuracy loss in depth-pruned large language models.
Reality Card
Core Claim
SHIFT-LLM recovers accuracy lost to depth pruning by using Linear Residual Adapters, achieving gains up to +15.7 points on Llama-3.1-8B-Instruct.
Method / Result
Achieves accuracy recovery with only a few hundred calibration samples and no gradient computation.
Limitations
The method relies on a small held-out set for calibration, which may limit generalizability across different datasets.
Paper to code
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