Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering

• URL: http://arxiv.org/abs/2604.16790v1
• Date: Sat, 18 Apr 2026 02:35:05 GMT
• Title: Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models are increasingly used as judges to evaluate code artifacts when exhaustive human review or executable test coverage is unavailable. LLM-judge is increasingly relevant in agentic software engineering workflows, where it can help rank candidate solutions and guide patch selection. While attractive for scale, current practice lacks a principled account of reliability and bias: repeated evaluations of the same case can disagree; small prompt edits can swing outcomes; and seemingly semantics-preserving, human-equivalent perturbations may elicit divergent verdicts. This paper studies LLM-as-a-Judge for code through a measurement-first lens. We analyze two pointwise judging regimes across code generation, code repair task, and test generation, and we systematically probe prompt-induced biases. Our study considers difficulty levels for repeated runs and controlled prompt interventions that isolate one presentation cue at a time, and it evaluates judges using consistency and sensitivity to bias. We find that judge decisions are highly sensitive to prompt biases even when the underlying code snippet is unchanged. Across all three tasks, several biases systematically shift preferences toward the option favored by the prompt, improving accuracy when that option aligns with the gold answer but substantially reducing it otherwise. In some settings, these effects are large enough to change task-level conclusions and alter relative model rankings. These findings show that reported judge performance may reflect prompt artifacts rather than stable assessment ability, posing a direct threat to the validity and reproducibility of code evaluation. We therefore argue that LLM-as-a-Judge studies should report bias sensitivity alongside accuracy and incorporate explicit controls to support more trustworthy model comparison in software engineering.

Related papers

• 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)
• Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation [76.5533899503582]
  Large language models (LLMs) are increasingly used as judges to evaluate agent performance.<n>We show this paradigm implicitly assumes that the agent's chain-of-thought (CoT) reasoning faithfully reflects both its internal reasoning and the underlying environment state.<n>We demonstrate that manipulated reasoning alone can inflate false positive rates of state-of-the-art VLM judges by up to 90% across 800 trajectories spanning diverse web tasks.
  arXiv  Detail & Related papers  (2026-01-21T06:07:43Z)
• 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)
• The Silent Judge: Unacknowledged Shortcut Bias in LLM-as-a-Judge [17.555073770285095]
  Large language models (LLMs) are increasingly deployed as automatic judges to evaluate system outputs in tasks such as summarization, dialogue, and creative writing.<n>We show that current LLM judges fail on both counts by relying on shortcuts introduced in the prompt.
  arXiv  Detail & Related papers  (2025-09-30T10:48:08Z)
• Reference-Free Rating of LLM Responses via Latent Information [53.463883683503106]
  We study the common practice of asking a judge model to assign Likert-scale scores to free-text responses.<n>We then propose and evaluate Latent Judges, which derive scalar ratings from internal model signals.<n>Across a broad suite of pairwise and single-rating benchmarks, latent methods match or surpass standard prompting.
  arXiv  Detail & Related papers  (2025-09-29T12:15:52Z)
• CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks [63.562924932512765]
  Large Language Models (LLMs) have advanced the state-of-the-art in various coding tasks.<n>LLMs can also serve as judges, assessing and comparing the quality of responses generated by other models.
  arXiv  Detail & Related papers  (2025-07-14T17:56:29Z)
• But what is your honest answer? Aiding LLM-judges with honest alternatives using steering vectors [0.0]
  Judge Using Safety-Steered Alternatives (JUSSA) is a framework that employs steering vectors during inference to generate more honest alternatives.<n>We evaluate JUSSA on sycophancy detection and introduce a new manipulation dataset covering multiple types of manipulation.<n>Our work opens new directions for scalable model auditing as systems become increasingly sophisticated.
  arXiv  Detail & Related papers  (2025-05-23T11:34:02Z)
• 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)
• Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators [66.83088028268318]
  This paper introduces the Judge Evaluation for Test-Time Scaling benchmark.<n>It evaluates judge performance in three domains (math reasoning, code generation, and instruction following) under three task settings.<n>Our benchmark shows that while judges are competitive with outcome reward models in reranking, they are consistently worse than process reward models in beam search procedures.
  arXiv  Detail & Related papers  (2025-04-21T17:33:23Z)
• 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)
• Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction [56.17020601803071]
  Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction. This paper aims to improve the reliability of existing benchmarks by thoroughly investigating and mitigating prompt bias.
  arXiv  Detail & Related papers  (2024-03-15T02:04:35Z)

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.