Auto-Prompt Ensemble for LLM Judge

• URL: http://arxiv.org/abs/2510.06538v1
• Date: Wed, 08 Oct 2025 00:28:51 GMT
• Title: Auto-Prompt Ensemble for LLM Judge
• Authors: Jiajie Li, Huayi Zhang, Peng Lin, Jinjun Xiong, Wei Xu,
• Abstract summary: Existing LLM judges often miss crucial evaluation dimensions because they fail to recognize the implicit standards underlying human assessments.<n>We propose the Auto-Prompt Ensemble (APE), an adaptive framework that automatically learns evaluation dimensions from its failure cases.<n>APE incorporates a confidence-based ensemble mechanism to decide when to adopt the judgments from additional evaluation dimensions.
• Score: 24.30935583220292
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a novel framework that improves the reliability of LLM judges by selectively augmenting LLM with auxiliary evaluation dimensions. Existing LLM judges often miss crucial evaluation dimensions because they fail to recognize the implicit standards underlying human assessments. To address this challenge, we propose the Auto-Prompt Ensemble (APE), an adaptive framework that automatically learns evaluation dimensions from its failure cases. APE incorporates a confidence-based ensemble mechanism to decide when to adopt the judgments from additional evaluation dimensions through a novel confidence estimation approach called Collective Confidence. Extensive experiments demonstrate that APE improves the reliability of LLM Judge across diverse standard benchmarks. For instance, APE enhances GPT-4o agreement rate on Reward Bench from 87.2% to 90.5% in the zero-shot setting. Overall, APE provides a principled approach for LLM Judge to leverage test-time computation, and bridge the evaluation gap between human and LLM judges.

Related papers

• Are We on the Right Way to Assessing LLM-as-a-Judge? [16.32248269615178]
  We introduce Sage, a novel evaluation suite that assesses the quality of LLM judges without requiring human annotation.<n>Inspired by axioms of rational choice theory, Sage introduces two new lenses for measuring LLM-as-a-Judge: local self-consistency and global logical consistency.<n>Based on Sage, we reveal that current state-of-the-art LLMs exhibit significant reliability problems when acting as judges in both scoring and pairwise settings.
  arXiv  Detail & Related papers  (2025-12-17T23:49:55Z)
• Who Judges the Judge? LLM Jury-on-Demand: Building Trustworthy LLM Evaluation Systems [2.9141470183751674]
  We propose a dynamic, learning-based framework for scalable and context-aware evaluation.<n>Our method trains a set of reliability predictors to assess when LLM judges will agree with human experts.<n> Experiments on summarization and RAG benchmarks show that our dynamic jury system achieves significantly higher correlation with human judgment than both single-judge and static-jury baselines.
  arXiv  Detail & Related papers  (2025-12-01T15:26:20Z)
• On Evaluating LLM Alignment by Evaluating LLMs as Judges [68.15541137648721]
  evaluating large language models' (LLMs) alignment requires them to be helpful, honest, safe, and to precisely follow human instructions.<n>We examine the relationship between LLMs' generation and evaluation capabilities in aligning with human preferences.<n>We propose a benchmark that assesses alignment without directly evaluating model outputs.
  arXiv  Detail & Related papers  (2025-11-25T18:33:24Z)
• Quantitative LLM Judges [48.676042957523045]
  We propose quantitative LLM judges, which align evaluation scores of existing LLM judges to human scores in a given domain.<n>The models are trained to improve the score of the original judge by using the judge's textual evaluation and score.<n>Our experiments show that quantitative judges can effectively improve the predictive power of existing judges through post-hoc modeling.
  arXiv  Detail & Related papers  (2025-06-03T14:44:23Z)
• J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization [69.23273504123941]
  We train judges to be robust to positional biases that arise in more complex evaluation settings.<n>We introduce ReasoningJudgeBench, a benchmark that evaluates judges in diverse reasoning settings not covered by prior work.<n>We train Judge for Reasoning (J4R), a 7B judge trained with EIS-GRPO that outperforms GPT-4o and the next best small judge by 6.7% and 9%.
  arXiv  Detail & Related papers  (2025-05-19T16:50:35Z)
• DAFE: LLM-Based Evaluation Through Dynamic Arbitration for Free-Form Question-Answering [12.879551933541345]
  We propose the Dynamic Arbitration Framework for Evaluation (DAFE) to evaluate large language models.<n>DAFE employs two primary LLM-as-judges and engages a third arbitrator only in cases of disagreements.<n>We show DAFE's ability to provide consistent, scalable, and resource-efficient assessments.
  arXiv  Detail & Related papers  (2025-03-11T15:29:55Z)
• Validating LLM-as-a-Judge Systems under Rating Indeterminacy [65.137380612741]
  We introduce a framework for validating LLM-as-a-judge systems under rating indeterminacy.<n>We demonstrate that differences in how humans and LLMs resolve rating indeterminacy when responding to forced-choice rating instructions can heavily bias validation.
  arXiv  Detail & Related papers  (2025-03-07T22:09:47Z)
• JudgeBench: A Benchmark for Evaluating LLM-based Judges [61.048125269475854]
  JudgeBench is a benchmark for evaluating LLM-based judges on challenging response pairs spanning knowledge, reasoning, math, and coding.<n>Our comprehensive evaluation on a collection of prompted judges, fine-tuned judges, multi-agent judges, and reward models shows that JudgeBench poses a significantly greater challenge than previous benchmarks.
  arXiv  Detail & Related papers  (2024-10-16T17:58:19Z)
• Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates [11.948519516797745]
  We develop an open-source framework to evaluate, compare, and visualize the reliability and alignment of LLM judges.<n>Our results indicate a significant impact of prompt templates on LLM judge performance, as well as a mediocre alignment level between the tested LLM judges and human evaluators.
  arXiv  Detail & Related papers  (2024-08-23T11:49:01Z)
• Evaluating the Evaluator: Measuring LLMs' Adherence to Task Evaluation Instructions [18.93335792080899]
  We investigate how much influence prompting the LLMs-as-a-judge has on the alignment of AI judgements to human judgements. We aggregate a taxonomy of quality criteria commonly used across state-of-the-art evaluations with LLMs and provide this as a rigorous benchmark of models as judges.
  arXiv  Detail & Related papers  (2024-08-16T14:49:35Z)
• Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussions [77.66677127535222]
  Auto-Arena is an innovative framework that automates the entire evaluation process using LLM-powered agents. In our experiments, Auto-Arena shows a 92.14% correlation with human preferences, surpassing all previous expert-annotated benchmarks.
  arXiv  Detail & Related papers  (2024-05-30T17:19:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.