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Random Matrix Theory detects overfitting in neural networks and LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method using Random Matrix Theory to detect overfitting in neural networks, particularly during the "anti-grokking" phase of long-horizon training. This technique identifies "Correlation Traps" within model layers by analyzing deviations from the Marchenko-Pastur distribution in randomized weight matrices. The study found that these traps increase as test accuracy declines while training accuracy remains high, and importantly, some large-scale LLMs exhibit similar traps, suggesting potential harmful overfitting. AI

IMPACT This new method could help developers identify and mitigate harmful overfitting in large language models, potentially improving their generalization and reliability.

RANK_REASON The cluster contains an academic paper detailing a new method for detecting overfitting in neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Random Matrix Theory detects overfitting in neural networks and LLMs

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The cluster contains an academic paper detailing a new method for detecting overfitting in neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Charles H Martin ·

    Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory

    Training Neural Networks (NNs) without overfitting is difficult; detecting that overfitting is difficult as well. We present a novel Random Matrix Theory method that detects the onset of overfitting in deep learning models without access to train or test data. For each model laye…

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