Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning

Step-by-step decision planning with large language models (LLMs) is gaining attention in AI agent development. This paper focuses on decision planning with uncertainty estimation to address the hallucination problem in language models. Existing approaches are either white-box or computationally demanding, limiting use of black-box proprietary LLMs within budgets. The paper's first contribution is a non-parametric uncertainty quantification method for LLMs, efficiently estimating point-wise dependencies between input-decision on the fly with a single inference, without access to token logits. This estimator …

agent attention box cs.ai cs.cl cs.lg decision development hallucination language language models large language large language models llms non-parametric paper parametric planning proprietary quantification step-by-step uncertainty