A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models

arXiv cs.AI Papers
uncertainty-estimation black-box large-language-models evaluation benchmark llm-reliability systematic-review

Summary

This paper presents a systematic review and benchmark of 24 black-box uncertainty estimation methods for large language models across 4 models and 4 dataset settings, finding that no single method dominates but hybrid methods that combine multiple uncertainty signals perform well.

arXiv:2606.19868v1 Announce Type: new Abstract: 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-based, sampling-based, explanation-based, multi-agent, and hybrid methods. We further build a unified evaluation framework and benchmark 24 representative methods across 4 models and 4 dataset settings. Our results show that no single method consistently dominates across all settings. Nevertheless, methods that reason over and compare candidates in the answer space are generally effective, and hybrid methods that combine multiple uncertainty signals perform well under most conditions. By releasing the benchmark data and a unified evaluation framework, we aim to facilitate reproducible comparisons and support future research, while our empirical findings provide practical guidance for developing future black-box UE methods for LLMs.
Original Article
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# A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models
Source: [https://arxiv.org/abs/2606.19868](https://arxiv.org/abs/2606.19868)
[View PDF](https://arxiv.org/pdf/2606.19868)

> Abstract: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\-based, sampling\-based, explanation\-based, multi\-agent, and hybrid methods\. We further build a unified evaluation framework and benchmark 24 representative methods across 4 models and 4 dataset settings\. Our results show that no single method consistently dominates across all settings\. Nevertheless, methods that reason over and compare candidates in the answer space are generally effective, and hybrid methods that combine multiple uncertainty signals perform well under most conditions\. By releasing the benchmark data and a unified evaluation framework, we aim to facilitate reproducible comparisons and support future research, while our empirical findings provide practical guidance for developing future black\-box UE methods for LLMs\.

## Submission history

From: Jiayi Wang \[[view email](https://arxiv.org/show-email/2ea5a357/2606.19868)\] **\[v1\]**Thu, 18 Jun 2026 07:27:34 UTC \(2,408 KB\)

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