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New method Optimizable Model Averaging enriches foundation model ensembles

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Optimizable Model Averaging (OMA), a novel black-box technique for ensembling foundation models. This method learns non-negative weights to combine predictive probabilities from different models without requiring fine-tuning or access to model parameters. OMA aims to improve accuracy and robustness in tasks like text and image classification by addressing the challenge of confidently incorrect models through regularization, offering better performance-calibration trade-offs than standard model output averaging. AI

IMPACT This method could enable more efficient and effective use of diverse foundation models for improved performance on complex tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for model ensembling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

New method Optimizable Model Averaging enriches foundation model ensembles

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The cluster contains an academic paper detailing a new method for model ensembling. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 Norsk(NO) · Mijung Park ·

    Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs

    arXiv:2505.21857v2 Announce Type: replace-cross Abstract: Foundation model ensembles can improve accuracy and robustness, but full fine-tuning and parameter-space merging are often expensive or impossible when candidate models have different architectures. We propose Optimizable …

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