Estimating the Black-box LLM Uncertainty with Distribution-Aligned Adversarial Distillation

• URL: http://arxiv.org/abs/2605.05777v1
• Date: Thu, 07 May 2026 07:09:29 GMT
• Title: Estimating the Black-box LLM Uncertainty with Distribution-Aligned Adversarial Distillation
• Abstract summary: We propose a generation-discrimination architecture to guide a lightweight proxy model to learn the high-quality regions of the output distribution of a large language model.<n>We use the proxy model to reproduce the specific responses of the black-box LLM and estimate the corresponding uncertainty based on evidence learning.
• Score: 26.477248267616527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have progressed rapidly in complex reasoning and question answering, yet LLM hallucination remains a central bottleneck that hinders practical deployment, especially for commercial black-box LLMs accessible only via APIs. Existing uncertainty quantification methods typically depend on computationally expensive multiple sampling or internal parameters, which prevents real-time estimation and fails to capture information implicit in the black-box reasoning process. To address this issue, we propose Distribution-Aligned Adversarial Distillation (DisAAD), which introduces a generation-discrimination architecture to guide a lightweight proxy model to learn the high-quality regions of the output distribution of the black-box LLM, thus effectively endowing it with the ability to know whether the black-box LLM knows or not. Subsequently, we use the proxy model to reproduce the specific responses of the black-box LLM and estimate the corresponding uncertainty based on evidence learning. Extensive experiments have verified the effectiveness and promise of our proposed method, indicating that a proxy model even one that only accounts for 1\% of the target LLM's size can achieve reliable uncertainty quantification.

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