How Many Parameters Does Your Task Really Need? Task Specific Pruning with LLM-Sieve

• URL: http://arxiv.org/abs/2505.18350v2
• Date: Sat, 04 Oct 2025 01:32:32 GMT
• Title: How Many Parameters Does Your Task Really Need? Task Specific Pruning with LLM-Sieve
• Authors: Waleed Reda, Abhinav Jangda, Krishna Chintalapudi,
• Abstract summary: Large Language Models (LLMs) are increasingly deployed for narrow tasks in resource-constrained settings.<n>We present LLM-Sieve, a framework that prunes LLMs down to the minimal parameter subset needed to preserve task performance.
• Score: 2.33361323991006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Large Language Models (LLMs) are increasingly deployed for narrow tasks in resource-constrained settings, a central question arises: how much of an LLM is truly necessary for a given task? We present LLM-Sieve, a framework that prunes LLMs down to the minimal parameter subset needed to preserve task performance. Our approach introduces two innovations: (i) output-aligned non-orthogonal projections, which yield more faithful low-rank approximations than traditional PCA/SVD by aligning directly with layer outputs; and (ii) adaptive pruning via a Genetic Algorithm, which automatically discovers matrix-specific pruning levels and exposes the uneven distribution of task-relevant knowledge. Across models from 3.8B to 70B parameters, LLM-Sieve removes 20-75% of weights with only 1-5% accuracy loss-substantially ahead of prior pruning methods. Beyond efficiency, our framework reveals bottleneck matrices that concentrate critical knowledge, suggesting architectural implications for future LLM design. LLM-Sieve integrates seamlessly with LoRA fine-tuning and quantization, enabling both efficient deployment and deeper understanding of knowledge organization in LLMs.

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