A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models
Although large language models (LLMs) have shown strong capabilities across a wide range of tasks, their outputs often remain unreliable and may contain hallucinations, making uncertainty estimation (UE) essential for building trustworthy LLMs. In practice, many mainstream LLMs are only accessible through restricted APIs, where internal signals such as logits and hidden states are unavailable, making black-box UE especially important. However, existing work on black-box UE for LLMs remains fragmented in methodology and lacks a unified empirical comparison. To address this gap, we present a systematic review of black-box UE methods and organize them into five categories: verbalization-base...
| Authors | Jiayi Wang, Xu-Yao Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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