Black-box Uncertainty Quantification Method for LLM-as-a-Judge
LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty of LLM-as-a-Judge evaluations. While uncertainty quantification has been well-studied in other domains, applying it effectively to LLMs poses unique challenges due to their complex decision-making capabilities and computational demands. In this paper, we introduce a novel method for quantifying uncertainty designed to enhance the trustworthiness of LLM-as-a-Judge evaluations. The method quantifies uncertainty by analyzing the relationships between generated assessments and possible ratings. By cross-evaluating these relationships and constructing a confusion matrix based on token probabilities, the method derives labels of high or low uncertainty. We evaluate our method across multiple benchmarks, demonstrating a strong correlation between the accuracy of LLM evaluations and the derived uncertainty scores. Our findings suggest that this method can significantly improve the reliability and consistency of LLM-as-a-Judge evaluations.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingUncertainty QuantificationSimilar Papers 제목 키워드 기반
Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers
Hallucinations are a persistent problem with Large Language Models (LLMs). As these models become increasingly used in high-stakes domains, such as healthcare and finance, the need for effective hallucination detection i…
HallucinationQuestion AnsweringUncertainty QuantificationEstimating 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 …
Question AnsweringMAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty
Despite the massive advancements in large language models (LLMs), they still suffer from producing plausible but incorrect responses. To improve the reliability of LLMs, recent research has focused on uncertainty quantif…
Mathematical ReasoningQuestion AnsweringUncertainty QuantificationWorld KnowledgeOutput-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
We introduce a class of acquisition functions for sample selection that leads to faster convergence in applications related to Bayesian experimental design and uncertainty quantification. The approach follows the paradig…
Active LearningExperimental DesignUncertainty QuantificationBeyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation
Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT). Traditionally, a QE system accepts the original source text and translation from a black-box MT system as input. Recently, a fe…
Feature EngineeringLanguage ModelingLanguage ModellingMachine Translation+2