Estimating the Black-box LLM Uncertainty with Distribution-Aligned Adversarial Distillation
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...
| Authors | Huizi Cui, Huan Ma, Qilin Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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