arXiv:2601.22068v2 Announce Type: replace Abstract: Foundation models have become a dominant paradigm in machine learning, achieving remarkable performance across diverse tasks through large-scale pretraining. However, they often yield overconfident, uncalibrated predictions. The standard approach to quantifying epistemic uncertainty are ensembles of multiple independently trained models. But their computational cost scales linearly with ensemble size, making them impractical for large foundation models. We propose Singular Value Ensemble (SVE), a parameter-efficient implicit ensembling method
The proliferation of powerful, large foundation models necessitates efficient methods to quantify their inherent uncertainty and improve reliability, which is critical for their deployment in sensitive applications.
Improving the trustworthiness and reliability of foundation models by addressing their overconfidence is crucial for broad adoption and mitigating risks in high-stakes environments.
The proposed Singular Value Ensemble method offers a computationally efficient way to quantify epistemic uncertainty in foundation models, potentially enabling more responsible and widespread AI deployment.
- · AI developers
- · Foundation model users
- · AI safety researchers
- · Cloud computing providers
- · Traditional ensemble methods
- · AI applications requiring high certainty with limited computational resources
More reliable and less 'overconfident' foundation models become accessible for various industries.
This could accelerate the deployment of AI in critical infrastructure and decision-making systems.
Increased trust in AI might lead to a further collapse of certain white-collar workflows as autonomous agents become more viable.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG