Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - WSJ
<a href="https://news.google.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?oc=5" target="_blank">Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models</a> <font color="#6f6f6f">WSJ</font>
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arXiv:2604.03258v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive capabilities across various tasks, but the billion-scale parameters pose deployment challenges. Although existing methods attempt to reduce the scale of LLMs, they require either special hardware support or expensive post-training to maintain model quality. To facilitate efficient and affordable model slimming, we propose a novel training-free compression method for LLMs, named "SoLA", which leverages \textbf{So}ft activation sparsity and \textbf{L}ow-r\textbf{A}nk decomposition. SoLA can identify and retain a minority of components significantly contributing to inference, while compressing the majority through low-rank decomposition, based on our analysis of the activation pattern in
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SoLA: Leveraging Soft Activation Sparsity and Low-Rank Decomposition for Large Language Model Compression
arXiv:2604.03258v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive capabilities across various tasks, but the billion-scale parameters pose deployment challenges. Although existing methods attempt to reduce the scale of LLMs, they require either special hardware support or expensive post-training to maintain model quality. To facilitate efficient and affordable model slimming, we propose a novel training-free compression method for LLMs, named "SoLA", which leverages \textbf{So}ft activation sparsity and \textbf{L}ow-r\textbf{A}nk decomposition. SoLA can identify and retain a minority of components significantly contributing to inference, while compressing the majority through low-rank decomposition, based on our analysis of the activation pattern in
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arXiv:2604.03257v1 Announce Type: new Abstract: The ability to rigorously estimate the failure rates of large language models (LLMs) is a prerequisite for their safe deployment. Currently, however, practitioners often face a tradeoff between expensive human gold standards and potentially severely-biased automatic annotation schemes such as "LLM-as-a-Judge" labeling. In this paper, we propose a new, practical, and efficient approach to LLM failure rate estimation based on constrained maximum-likelihood estimation (MLE). Our method integrates three distinct signal sources: (i) a small, high-quality human-labeled calibration set, (ii) a large corpus of LLM-judge annotations, and, most importantly, (iii) additional side information via domain-specific constraints derived from known bounds on j
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