Summary of Zeroth-order Adaptive Neuron Alignment Based Pruning Without Re-training, by Elia Cunegatti et al.
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training
by Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed algorithm, NeuroAL, is a top-up method that can be used with any pruning algorithm for Large Language Models (LLMs) to obtain sparse models that maximize neuron alignment among activations. Unlike existing methods, NeuroAL adaptively selects the best hyperparameters for block-wise and row-wise sparsity ratios based on the model and desired sparsity, requiring no re-training. The approach is tested across 276 cases combining four LLM families, three sparsity ratios, and ten language tasks, showing consistent outperformance of state-of-the-art methods in terms of performance-runtime trade-off. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeuroAL is a new way to make large language models smaller and faster without losing their ability to understand and generate text. Normally, making these models smaller would require re-training them, but NeuroAL does it without needing to start over. This approach works well with different types of language models and tasks, and it’s better than current methods at balancing how well the model performs with how fast it is. |
Keywords
» Artificial intelligence » Alignment » Pruning
