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

As Large Language Models (LLMs) are increasingly being adopted for narrow tasks - such as medical question answering or sentiment analysis - and deployed in resource-constrained settings, a key question arises: how many parameters does a task actually need? In this work, we present LLM-Sieve, the first comprehensive framework for task-specific pruning of LLMs that achieves 20-75% parameter reduction with only 1-5% accuracy degradation across diverse domains. Unlike prior methods that apply uniform pruning or rely on low-rank approximations of weight matrices or inputs in isolation, LLM-Sieve (i) learns task-aware joint projections to better approximate output behavior, and (ii) employs a Genetic Algorithm to discover differentiated pruning levels for each matrix. LLM-Sieve is fully compatible with LoRA fine-tuning and quantization, and uniquely demonstrates strong generalization across datasets within the same task domain. Together, these results establish a practical and robust mechanism to generate smaller performant task-specific models.