Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge

• URL: http://arxiv.org/abs/2406.07791v8
• Date: Thu, 17 Apr 2025 02:43:35 GMT
• Title: Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge
• Abstract summary: This study evaluates position bias in LLM judges across pairwise and list-wise comparison settings.<n>We identify Judge-Level, Candidate-Level, and Task-Level factors contributing to bias.
• Score: 24.206975650221732
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: LLM-as-a-Judge has emerged as a promising alternative to human evaluators across various tasks, yet inherent biases - particularly position bias, the tendency to favor solutions based on their position within the prompt - compromise its reliability. This exploratory study evaluates position bias in LLM judges across pairwise and list-wise comparison settings, introducing three metrics: repetition stability, position consistency, and preference fairness. Our experiments, involving 15 LLM judges across MTBench and DevBench with 22 tasks and approximately 40 solution-generating models, result in over 150,000 evaluation instances. We identify Judge-Level, Candidate-Level, and Task-Level factors contributing to bias. The findings confirm that position bias is not due to random chance and varies significantly across judges and tasks. While position bias is weakly influenced by the length of prompt components, it is strongly affected by the quality gap between solutions. Our agreement and disagreement analysis among judges further provides insights into the distribution of judging difficulty across the dataset, and highlights the potential for dataset modifications.

Related papers

• Can We Trust LLM Judges: A Study of Capability-Dependent Biases and Multi-Judge Ensemble for Bias Calibration [0.5294698352039444]
  We study absolute scoring tasks, which mirror more realistic use cases.<n>We show that a model's task accuracy strongly predicts its judging accuracy.<n>More capable examinee models consistently receive more lenient judgments from all judges.
  arXiv  Detail & Related papers  (2026-09-10T00:25:39Z)
• Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering [0.0]
  Large Language Models are increasingly used as judges to evaluate code artifacts.<n>Current practice lacks a principled account of reliability and bias.<n>This paper studies LLM-as-a-Judge for code through a measurement-first lens.
  arXiv  Detail & Related papers  (2026-04-18T02:35:05Z)
• Toward Robust LLM-Based Judges: Taxonomic Bias Evaluation and Debiasing Optimization [44.252712888022835]
  Large language model (LLM)-based judges are widely adopted for automated evaluation and reward modeling.<n>We propose JudgeBiasBench, a benchmark for systematically quantifying biases in LLM-based judges.<n>We conduct experiments across both generative and discriminative judges, revealing that current judges exhibit significant and diverse bias patterns.
  arXiv  Detail & Related papers  (2026-03-09T08:32:21Z)
• BiasScope: Towards Automated Detection of Bias in LLM-as-a-Judge Evaluation [31.32711926164484]
  BiasScope is a framework for automatically and at scale discovering potential biases during model evaluation.<n>JudgeBench-Pro is a more challenging benchmark for evaluating the robustness of LLM-as-a-judge.
  arXiv  Detail & Related papers  (2026-02-10T03:51:03Z)
• FairJudge: An Adaptive, Debiased, and Consistent LLM-as-a-Judge [10.584937371987742]
  Existing LLM-as-a-Judge systems suffer from limited adaptivity to task- and domain-specific evaluation criteria.<n>We propose FairJudge, an adaptive, debiased, and consistent LLM-as-a-Judge.
  arXiv  Detail & Related papers  (2026-02-06T11:35:32Z)
• 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)
• Evaluating and Mitigating LLM-as-a-judge Bias in Communication Systems [32.83708359216193]
  Large Language Models (LLMs) are increasingly being used to autonomously evaluate the quality of content in communication systems.<n>This paper systematically investigates judgment biases in two LLM-as-a-judge models under the point-wise scoring setting.<n>We propose four potential mitigation strategies to ensure fair and reliable AI judging in practical communication scenarios.
  arXiv  Detail & Related papers  (2025-10-14T12:52:29Z)
• Hearing the Order: Investigating Selection Bias in Large Audio-Language Models [51.69003519291754]
  Large audio-language models (LALMs) are often used in tasks that involve reasoning over ordered options.<n>In this paper, we identify and analyze this problem in LALMs.
  arXiv  Detail & Related papers  (2025-10-01T08:00:58Z)
• CompassJudger-2: Towards Generalist Judge Model via Verifiable Rewards [72.44810390478229]
  CompassJudger-2 is a novel generalist judge model that overcomes limitations via a task-driven, multi-domain data curation strategy.<n> CompassJudger-2 achieves superior results across multiple judge and reward benchmarks.
  arXiv  Detail & Related papers  (2025-07-12T01:34:24Z)
• Evaluating Scoring Bias in LLM-as-a-Judge [8.751901240110888]
  Large Language Models (LLMs) are employed as evaluators for complex tasks.<n>There are various biases within LLM-as-a-Judge, which adversely affect the fairness and reliability of judgments.
  arXiv  Detail & Related papers  (2025-06-27T15:25:23Z)
• Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplifications and Resistance in Multi-Agent Based LLM-as-Judge [37.06831535578792]
  We conduct a systematic analysis of four diverse bias types: position bias, verbosity bias, chain-of-thought bias, and bandwagon bias.<n>We evaluate these biases across two widely adopted multi-agent LLM-as-Judge frameworks: Multi-Agent-Debate and LLM-as-Meta-Judge.
  arXiv  Detail & Related papers  (2025-05-26T03:56:41Z)
• Assessing Judging Bias in Large Reasoning Models: An Empirical Study [99.86300466350013]
  Large Reasoning Models (LRMs) like DeepSeek-R1 and OpenAI-o1 have demonstrated remarkable reasoning capabilities. We present a benchmark comparing judging biases between LLMs and LRMs across both subjective preference-alignment datasets and objective fact-based datasets.
  arXiv  Detail & Related papers  (2025-04-14T07:14:27Z)
• JuStRank: Benchmarking LLM Judges for System Ranking [7.507819077549208]
  We conduct the first large-scale study of LLM judges as system rankers. System scores are generated by aggregating judgment scores over multiple system outputs. Our analysis provides a fine-grained characterization of judge behavior, including their decisiveness and bias.
  arXiv  Detail & Related papers  (2024-12-12T18:51:13Z)
• Diverging Preferences: When do Annotators Disagree and do Models Know? [92.24651142187989]
  We develop a taxonomy of disagreement sources spanning 10 categories across four high-level classes. We find that the majority of disagreements are in opposition with standard reward modeling approaches. We develop methods for identifying diverging preferences to mitigate their influence on evaluation and training.
  arXiv  Detail & Related papers  (2024-10-18T17:32:22Z)
• 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. 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)
• Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge [84.34545223897578]
  Despite their excellence in many domains, potential issues are under-explored, undermining their reliability and the scope of their utility. We identify 12 key potential biases and propose a new automated bias quantification framework-CALM- which quantifies and analyzes each type of bias in LLM-as-a-Judge. Our work highlights the need for stakeholders to address these issues and remind users to exercise caution in LLM-as-a-Judge applications.
  arXiv  Detail & Related papers  (2024-10-03T17:53:30Z)
• Mitigating the Bias of Large Language Model Evaluation [30.67730115141905]
  We propose systematic research about the bias of LLM-as-a-Judge. For closed-source judge models, we apply calibration to mitigate the significance of superficial quality. For open-source judge models, we propose to mitigate the bias by contrastive training, with curated negative samples that deviate from instruction but present better superficial quality.
  arXiv  Detail & Related papers  (2024-09-25T09:52:44Z)
• Direct Judgement Preference Optimization [66.83088028268318]
  We train large language models (LLMs) as generative judges to evaluate and critique other models' outputs. We employ three approaches to collect the preference pairs for different use cases, each aimed at improving our generative judge from a different perspective. Our model robustly counters inherent biases such as position and length bias, flexibly adapts to any evaluation protocol specified by practitioners, and provides helpful language feedback for improving downstream generator models.
  arXiv  Detail & Related papers  (2024-09-23T02:08:20Z)
• From Calculation to Adjudication: Examining LLM judges on Mathematical Reasoning Tasks [11.01213914485374]
  We study large language models (LLMs) on mathematical reasoning tasks. Our analysis uncovers a strong correlation between judgment performance and the candidate model task performance. We show that it is possible to use statistics, such as the task performances of the individual models, to predict judgment performance.
  arXiv  Detail & Related papers  (2024-09-06T10:09:41Z)
• Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates [10.091146498861333]
  Commercial large language models (LLMs) like GPT-4 have been recently employed to evaluate and compare different alignment approaches. We develop a framework to evaluate, compare, and visualize the reliability and alignment of LLM judges.
  arXiv  Detail & Related papers  (2024-08-23T11:49:01Z)
• CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models [58.57987316300529]
  Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks. To evaluate the biases exhibited by LLMs, researchers have recently proposed a variety of datasets. We propose CEB, a Compositional Evaluation Benchmark that covers different types of bias across different social groups and tasks.
  arXiv  Detail & Related papers  (2024-07-02T16:31:37Z)
• Eliminating Position Bias of Language Models: A Mechanistic Approach [119.34143323054143]
  Position bias has proven to be a prevalent issue of modern language models (LMs) Our mechanistic analysis attributes the position bias to two components employed in nearly all state-of-the-art LMs: causal attention and relative positional encodings. By eliminating position bias, models achieve better performance and reliability in downstream tasks, including LM-as-a-judge, retrieval-augmented QA, molecule generation, and math reasoning.
  arXiv  Detail & Related papers  (2024-07-01T09:06:57Z)
• Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators [48.54465599914978]
  Large Language Models (LLMs) have demonstrated promising capabilities in assessing the quality of generated natural language. LLMs still exhibit biases in evaluation and often struggle to generate coherent evaluations that align with human assessments. We introduce Pairwise-preference Search (PairS), an uncertainty-guided search method that employs LLMs to conduct pairwise comparisons and efficiently ranks candidate texts.
  arXiv  Detail & Related papers  (2024-03-25T17:11:28Z)

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.