No-Knowledge Alarms for Misaligned LLMs-as-Judges
- URL: http://arxiv.org/abs/2509.08593v1
- Date: Wed, 10 Sep 2025 13:46:40 GMT
- Title: No-Knowledge Alarms for Misaligned LLMs-as-Judges
- Abstract summary: We develop no-knowledge alarms for misaligned LLM judges.<n>The alarms can detect, with no false positives, that at least one member or more of an ensemble of judges are violating a user specified grading ability requirement.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: If we use LLMs as judges to evaluate the complex decisions of other LLMs, who or what monitors the judges? Infinite monitoring chains are inevitable whenever we do not know the ground truth of the decisions by experts and we do not want to trust them. One way to ameliorate our evaluation uncertainty is to exploit the use of logical consistency between disagreeing experts. By observing how LLM judges agree and disagree while grading other LLMs, we can compute the only possible evaluations of their grading ability. For example, if two LLM judges disagree on which tasks a third one completed correctly, they cannot both be 100\% correct in their judgments. This logic can be formalized as a Linear Programming problem in the space of integer response counts for any finite test. We use it here to develop no-knowledge alarms for misaligned LLM judges. The alarms can detect, with no false positives, that at least one member or more of an ensemble of judges are violating a user specified grading ability requirement.
Related papers
- When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability [0.0]
An LLM-as-judge score can move even when the candidate responses stay fixed, simply because the evaluator has changed.<n>We compare two upgrade paths available in practice: scaling Qwen3 dense judges from 1.7B to 32B parameters and moving across MiniMax M2-M2.7 released APIs.<n>The main pattern is that judge upgrades are not interchangeable: only Qwen3 1.7B to 4B gives a robust adjacent gain, while MiniMax releases do not.
arXiv Detail & Related papers (2026-07-09T14:31:05Z) - Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training [75.98427023288052]
Reasoning LLMs-as-Judges provide a promising path for extending the success of reasoning models to non-verifiable domains.<n>This study investigates the actual impact of non-reasoning and reasoning judges in reinforcement-learning-based LLM alignment.<n>We find that reasoning-judge-trained policies achieve such strong performance by learning to generate highly effective adversarial outputs.
arXiv Detail & Related papers (2026-03-12T17:57:06Z) - Judge Reliability Harness: Stress Testing the Reliability of LLM Judges [1.1699027359021665]
Judge Reliability Harness is an open source library for constructing validation suites that test the reliability of LLM judges.<n>We evaluate four state-of-the-art judges across four benchmarks spanning safety, persuasion, misuse, and agentic behavior.
arXiv Detail & Related papers (2026-03-05T17:27:07Z) - Logical Consistency Between Disagreeing Experts and Its Role in AI Safety [0.0]
Two experts disagree on a test, we may conclude both cannot be 100 per cent correct. But if they completely agree, no possible evaluation can be excluded.<n>This asymmetry in the utility of agreements versus disagreements is explored here by formalizing a logic of unsupervised evaluation for classifiers.
arXiv Detail & Related papers (2025-10-01T12:30:01Z) - Quantitative LLM Judges [60.773734899532336]
We propose quantitative LLM judges, which align evaluation scores of existing LLM judges to humans in a given domain.<n>The models are trained to improve the score of the original judge using its rationale and score.<n>Our experiments show that quantitative judges can improve the predictive power of existing judges through post-hoc modeling.
arXiv Detail & Related papers (2025-06-03T14:44:23Z) - Judging LLMs on a Simplex [2.088672652658465]
A common practice is to use large language models (LLMs) themselves as judges, but the theoretical properties of this approach are not yet well understood.<n>We show that a geometric framework that represents both judges and candidates as points on a probability simplex can provide helpful insight on what is or is not identifiable.
arXiv Detail & Related papers (2025-05-28T04:50:41Z) - Arbiters of Ambivalence: Challenges of Using LLMs in No-Consensus Tasks [52.098988739649705]
This study examines the biases and limitations of LLMs in three roles: answer generator, judge, and debater.<n>We develop a no-consensus'' benchmark by curating examples that encompass a variety of a priori ambivalent scenarios.<n>Our results show that while LLMs can provide nuanced assessments when generating open-ended answers, they tend to take a stance on no-consensus topics when employed as judges or debaters.
arXiv Detail & Related papers (2025-05-28T01:31:54Z) - Don't Judge Code by Its Cover: Exploring Biases in LLM Judges for Code Evaluation [14.521056434373213]
Using large language models as evaluators has expanded to code evaluation tasks.<n>This raises a critical, unresolved question: Can LLM judges fairly and robustly evaluate semantically equivalent code with superficial variations?<n>We present the first comprehensive study of this issue, defining six types of potential bias in code evaluation.
arXiv Detail & Related papers (2025-05-22T04:49:33Z) - Leveraging LLMs as Meta-Judges: A Multi-Agent Framework for Evaluating LLM Judgments [6.270885758858811]
Large language models (LLMs) are being widely applied across various fields, but as tasks become more complex, evaluating their responses is increasingly challenging.<n>We propose a three-stage meta-judge selection pipeline: 1) developing a comprehensive rubric with GPT-4 and human experts, 2) using three advanced LLM agents to score judgments, and 3) applying a threshold to filter out low-scoring judgments.<n> Experimental results on the JudgeBench dataset show about 15.55% improvement compared to raw judgments and about 8.37% improvement over the single-agent baseline.
arXiv Detail & Related papers (2025-04-23T20:32:12Z) - 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 show that different measures of judge system performance under different human--judge agreement metrics, and different rating elicitation and aggregation schemes bias validation approaches.
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) - JudgeLM: Fine-tuned Large Language Models are Scalable Judges [48.053949045598344]
evaluating Large Language Models (LLMs) in open-ended scenarios is challenging because existing benchmarks and metrics can not measure them comprehensively.<n>We propose to fine-tune LLMs as scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in open-ended benchmarks.<n>We train JudgeLM at different scales from 7B, 13B, to 33B parameters, and conduct a systematic analysis of its capabilities and behaviors.
arXiv Detail & Related papers (2023-10-26T17:48:58Z) - A Comprehensive Evaluation of Large Language Models on Legal Judgment
Prediction [60.70089334782383]
Large language models (LLMs) have demonstrated great potential for domain-specific applications.
Recent disputes over GPT-4's law evaluation raise questions concerning their performance in real-world legal tasks.
We design practical baseline solutions based on LLMs and test on the task of legal judgment prediction.
arXiv Detail & Related papers (2023-10-18T07:38:04Z)
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.