Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
arXiv:2603.10396v1 Announce Type: new Abstract: Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not always adequately captured by the elicitation techniques developed under the classical probabilistic uncertainty framework. This mismatch leads to systematic failure modes, particularly in settings that involve ambiguous question-answering, in-context learning, and self-reflection. To address this, we propose novel prompt-based uncertainty elicitation techniques grounded in \emph{imprecise probabilities}, a principled framework for repesenting and eliciting higher-order uncertainty. Here, first-order uncertainty captures uncertainty over possible responses to a prompt, while second-order uncertainty (uncertainty about uncertainty) quantifies indeterminacy in the underlying probability model itself. We introduce general-purpose prompting and post-processing procedures to directly elicit and quantify
arXiv:2603.10396v1 Announce Type: new Abstract: Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not always adequately captured by the elicitation techniques developed under the classical probabilistic uncertainty framework. This mismatch leads to systematic failure modes, particularly in settings that involve ambiguous question-answering, in-context learning, and self-reflection. To address this, we propose novel prompt-based uncertainty elicitation techniques grounded in \emph{imprecise probabilities}, a principled framework for repesenting and eliciting higher-order uncertainty. Here, first-order uncertainty captures uncertainty over possible responses to a prompt, while second-order uncertainty (uncertainty about uncertainty) quantifies indeterminacy in the underlying probability model itself. We introduce general-purpose prompting and post-processing procedures to directly elicit and quantify both orders of uncertainty, and demonstrate their effectiveness across diverse settings. Our approach enables more faithful uncertainty reporting from LLMs, improving credibility and supporting downstream decision-making.
Executive Summary
The article proposes a novel approach to eliciting uncertainty from large language models (LLMs) using imprecise probabilities, which capture both first-order uncertainty over possible responses and second-order uncertainty about the underlying probability model. The authors introduce prompt-based techniques to quantify both orders of uncertainty, demonstrating their effectiveness across diverse settings. This approach enables more faithful uncertainty reporting from LLMs, improving credibility and supporting downstream decision-making. The proposed framework addresses the limitations of classical probabilistic uncertainty techniques, which often fail to capture LLM behavior adequately.
Key Points
- ▸ Imprecise probabilities are used to capture higher-order uncertainty in LLMs
- ▸ Prompt-based techniques are introduced to elicit and quantify first-order and second-order uncertainty
- ▸ The approach improves uncertainty reporting from LLMs, enhancing credibility and decision-making
Merits
Principled Framework
The use of imprecise probabilities provides a principled framework for representing and eliciting higher-order uncertainty, addressing the limitations of classical probabilistic techniques
Demerits
Complexity
The introduction of imprecise probabilities and prompt-based techniques may add complexity to the uncertainty elicitation process, potentially requiring additional computational resources and expertise
Expert Commentary
The article presents a significant contribution to the field of uncertainty quantification in LLMs, addressing a critical limitation of current techniques. The use of imprecise probabilities and prompt-based techniques offers a promising approach to capturing higher-order uncertainty, which can have far-reaching implications for AI applications. However, further research is needed to fully explore the potential benefits and challenges of this approach, particularly in terms of scalability, interpretability, and integration with existing AI systems.
Recommendations
- ✓ Further investigation into the scalability and interpretability of the proposed approach
- ✓ Exploration of the potential applications and limitations of imprecise probabilities in other AI domains