arXiv:2603.29466v2 Announce Type: replace-cross Abstract: Existing methods for quantifying predictive uncertainty in neural networks are either computationally intractable for large language models or require access to training data that is typically unavailable. We derive a lightweight alternative through two approximations: a first-order Taylor expansion that expresses uncertainty in terms of the gradient of the prediction and the parameter covariance, and an isotropy assumption on the parameter covariance. Together, these yield epistemic uncertainty as the squared gradient norm and aleatori
The increasing scale and deployment of large language models necessitate more efficient and accessible methods for uncertainty quantification to ensure reliability and safety.
This research addresses a critical limitation in current AI systems, enabling better decision-making in real-world applications by providing a lightweight method for understanding model confidence.
The ability to quantify uncertainty in large language models without extensive computational resources or proprietary training data democratizes access to robust AI applications.
- · AI developers
- · Cloud AI providers
- · Sectors reliant on AI safety and reliability (e.g., healthcare, finance)
- · Competitors with computationally intensive uncertainty methods
More reliable and trustworthy AI applications become feasible across various industries.
Reduced barriers to entry for developing and deploying AI in highly regulated or sensitive domains.
Increased adoption of AI in critical infrastructure and decision-making systems due to enhanced safety guarantees.
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Read at arXiv cs.CL