Estimating the Black-box LLM Uncertainty with Distribution-Aligned Adversarial Distillation (2026.acl-long)
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| Challenge: | Existing uncertainty quantification methods 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. |
| Approach: | They propose a distribution-aligned adjudication architecture to guide a lightweight proxy model to learn the high-quality regions of the output distribution of the black-box LLM. |
| Outcome: | Extensive experiments show that a proxy model even with 1% of the target LLM’s size can achieve reliable uncertainty quantification. |
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