Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence
arXiv:2606.19353v1 Announce Type: new Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations. Uncertainty decomposition-separating aleatoric from epistemic sources-is particularly crucial in this setting, yet existing methods, designed for standard generation tasks, fail to capture the unique dynamics of ICL. To address this, we introdu
The rapid deployment and increasing reliance on large language models in diverse applications necessitates robust methods for evaluating their output reliability, especially in critical contexts.
This development addresses a fundamental limitation in current AI applications by providing a clearer understanding of model confidence, crucial for improving safety, interpretability, and trust in LLM predictions.
The ability to quantify and decompose uncertainty in in-context learning will allow for more reliable deployment of LLMs, moving beyond qualitative assessments of their performance sensitivity.
- · AI developers
- · High-stakes AI applications
- · Enterprises adopting LLMs
- · AI safety researchers
- · Developers of less robust uncertainty quantification methods
- · Applications that rely on naive LLM output without confidence measures
Improved decision-making from AI systems due to better understanding of prediction confidence.
Accelerated integration of LLMs into regulated industries requiring auditable uncertainty metrics.
New certification standards and regulatory frameworks for AI systems based on their ability to transparently quantify prediction uncertainty.
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