---
title: "判断、检索还是弃权：具有可证明风险保证的不确定性守卫LLM评判"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.17994v1"
permalink: "https://feed.astralor.com/items/itm_3aaf3c66fccdd44f17f66e5b"
published: "2026-08-18T16:42:02.000Z"
score: 78
channel: "ai"
tags: ["arXiv","Clopper-Pearson","LLM","开源问答"]
---

# 判断、检索还是弃权：具有可证明风险保证的不确定性守卫LLM评判

## AI 点评

> 这篇论文切中LLM裁判在客观任务上‘不敢信’的要害。对做评测平台或Agent质量控制的团队来说，框架可直接作为风控层，不必再盲信单次输出。

## 速览

该论文提出一种风险受控的LLM裁判框架，在无参考答案的客观任务中，通过校准不确定性阈值，将接受裁决的假发现率控制在用户指定水平α以下。当参数化模式不够自信时，实例被路由到检索增强模式，利用网络证据重新评估。两阈值路由在有限样本下保持统计保证，无需额外假设。在开放域问答基准和多种规模裁判模型上，该方法在维持目标错误率的同时，覆盖率显著高于单模式基线。

## 关键点

- 提出风险受控的LLM裁判框架，通过校准不确定性阈值控制接受裁决的假发现率。
- 采用双模式路由：参数化模式不自信时切换至检索增强模式，并引入第二校准阈值。
- 使用Clopper-Pearson区间提供有限样本保证，无需额外假设。
- 在开放域QA基准上，相比单模式基线，在维持错误率的同时显著提升覆盖率。

## 全文

Using LLMs as judges has become standard practice for evaluating model outputs at scale. This is particularly common for subjective, open-ended tasks such as assessing helpfulness or alignment, where no single reference answer exists. However, objective tasks introduce a distinct reliability challenge for reference-free LLM judging. In the absence of a reference answer, the judge evaluates factual correctness either through its parametric knowledge or through tool augmentation. Although the former enables efficient evaluation, the judge may hallucinate or lack sufficient evidence for its verdict. Conversely, tool augmentation can provide additional evidence but introduces extra computational cost and requires an appropriate mechanism to determine when and how that evidence should be used reliably. More importantly, neither approach alone provides formal control over the risk of accepted verdicts or guarantees their reliability at a specified level. We propose a risk-controlled framework that calibrates uncertainty thresholds on a held-out set so that the false discovery rate among accepted verdicts remains below a user-specified level~$α$ with high probability, using finite-sample Clopper--Pearson intervals. When the parametric mode is not sufficiently confident, the instance is routed to a retrieval-augmented mode, where the judge gathers web evidence and re-evaluates the instance under a second calibrated threshold. The finite-sample guarantee carries over to this two-threshold routing without additional assumptions. Across open-domain QA benchmarks and judges of varying scales, the framework maintains the target error rate while achieving substantially higher coverage than single-mode baselines.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_3aaf3c66fccdd44f17f66e5b