Paper
Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score
Authors
Jimyung Hong, Jaehyung Kim
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deployment. Structured pruning offers a promising solution by removing entire dimensions or layers, yet existing methods face critical trade-offs: task-agnostic approaches cannot adapt to task-specific requirements, while task-aware methods require costly training to learn task adaptability. We propose DIET (Dimension-wise global pruning of LLMs via merging Task-wise importance scores), a training-free structured pruning method that combines dimension-level granularity with task-aware selection. DIET profiles activation magnitudes across tasks using only 100 samples per task, then applies majority voting to construct a single global mask. DIET does not require large costs from pre-computation or training. Experiments on seven zero-shot benchmarks using Gemma-2 2B and 9B models demonstrate the effectiveness of DIET; for example, at 20% sparsity on Gemma-2 2B, DIET achieves near 10% average accuracy improvement, compared to previous state-of-the-art structured pruning methods. This advantage persists across various sparsity levels and model scales, positioning DIET as a practical and robust choice for structured LLM pruning.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.23985v1</id>\n <title>Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score</title>\n <updated>2026-03-25T06:28:58Z</updated>\n <link href='https://arxiv.org/abs/2603.23985v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.23985v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deployment. Structured pruning offers a promising solution by removing entire dimensions or layers, yet existing methods face critical trade-offs: task-agnostic approaches cannot adapt to task-specific requirements, while task-aware methods require costly training to learn task adaptability. We propose DIET (Dimension-wise global pruning of LLMs via merging Task-wise importance scores), a training-free structured pruning method that combines dimension-level granularity with task-aware selection. DIET profiles activation magnitudes across tasks using only 100 samples per task, then applies majority voting to construct a single global mask. DIET does not require large costs from pre-computation or training. Experiments on seven zero-shot benchmarks using Gemma-2 2B and 9B models demonstrate the effectiveness of DIET; for example, at 20% sparsity on Gemma-2 2B, DIET achieves near 10% average accuracy improvement, compared to previous state-of-the-art structured pruning methods. This advantage persists across various sparsity levels and model scales, positioning DIET as a practical and robust choice for structured LLM pruning.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <published>2026-03-25T06:28:58Z</published>\n <arxiv:comment>14 pages, 10 figures. Code available at https://github.com/Jimmy145123/DIET</arxiv:comment>\n <arxiv:primary_category term='cs.LG'/>\n <author>\n <name>Jimyung Hong</name>\n </author>\n <author>\n <name>Jaehyung Kim</name>\n </author>\n </entry>"
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