Beyond Consensus: Mitigating the Agreeableness Bias in LLM Judge Evaluations

• URL: http://arxiv.org/abs/2510.11822v1
• Date: Mon, 13 Oct 2025 18:19:23 GMT
• Title: Beyond Consensus: Mitigating the Agreeableness Bias in LLM Judge Evaluations
• Authors: Suryaansh Jain, Umair Z. Ahmed, Shubham Sahai, Ben Leong,
• Abstract summary: New Large Language Models (LLMs) become available every few weeks, and modern application developers confront with the unenviable task of deciding if they should switch to a new model.<n>We show that while LLMs can identify valid outputs with high accuracy, they are remarkably poor at identifying invalid ones.<n>We introduce an optimal minority-veto strategy that is resilient to missing data and mitigates this bias to a large extent.
• Score: 0.20027036140258694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: New Large Language Models (LLMs) become available every few weeks, and modern application developers confronted with the unenviable task of having to decide if they should switch to a new model. While human evaluation remains the gold standard, it is costly and unscalable. The state-of-the-art approach is to use LLMs as evaluators ( LLM-as-a-judge), but this suffers from a critical flaw: LLMs exhibit a strong positive bias. We provide empirical evidence showing that while LLMs can identify valid outputs with high accuracy (i.e., True Positive Rate 96%), they are remarkably poor at identifying invalid ones (i.e., True Negative Rate <25%). This systematic bias, coupled with class imbalance, often leads to inflated reliability scores. While ensemble-based methods like majority voting can help, we show that they are not good enough. We introduce an optimal minority-veto strategy that is resilient to missing data and mitigates this bias to a large extent. For scenarios requiring even higher precision, we propose a novel regression-based framework that directly models the validator bias using a small set of human-annotated ground truth data. On a challenging code feedback task over 366 high-school Python programs, our regression approach reduces the maximum absolute error to just 1.2%, achieving a 2x improvement over the best-performing ensemble of 14 state-of-the-art LLMs.

Related papers

• Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation [89.52571224447111]
  Large language model (LLM) judges have often been used alongside traditional, algorithm-based metrics for tasks like summarization.<n>We provide an LLM judge bias analysis as a function of overlap with human-written responses in the domain of summarization.
  arXiv  Detail & Related papers  (2026-02-07T19:39:28Z)
• Towards Fair Large Language Model-based Recommender Systems without Costly Retraining [30.698816217416862]
  Large Language Models (LLMs) have revolutionized Recommender Systems (RS) through advanced generative user modeling.<n>LLMs-based RS (LLM-RS) often inadvertently perpetuates bias present in the training data, leading to severe fairness issues.<n>We propose FUDLR to reformulate the debiasing problem as an efficient machine unlearning task with two stages.
  arXiv  Detail & Related papers  (2026-01-24T15:40:52Z)
• Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation [66.84286617519258]
  Large language models are transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis.<n>Such variation can introduce systematic biases and random errors, which propagate to downstream analyses and cause Type I (false positive), Type II (false negative), Type S (wrong sign), or Type M (exaggerated effect) errors.<n>We find that intentional LLM hacking is strikingly simple. By replicating 37 data annotation tasks from 21 published social science studies, we show that, with just a handful of prompt paraphrases, virtually anything can be presented as statistically significant.
  arXiv  Detail & Related papers  (2025-09-10T17:58:53Z)
• Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers [59.168391398830515]
  We evaluate 12 pre-trained LLMs and one specialized fact-verifier, using a collection of examples from 14 fact-checking benchmarks.<n>We highlight the importance of addressing annotation errors and ambiguity in datasets.<n> frontier LLMs with few-shot in-context examples, often overlooked in previous works, achieve top-tier performance.
  arXiv  Detail & Related papers  (2025-06-16T10:32:10Z)
• DIF: A Framework for Benchmarking and Verifying Implicit Bias in LLMs [1.89915151018241]
  We argue that implicit bias in Large Language Models (LLMs) is not only an ethical, but also a technical issue.<n>We developed a method for calculating an easily interpretable benchmark, DIF (Demographic Implicit Fairness)
  arXiv  Detail & Related papers  (2025-05-15T06:53:37Z)
• 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)
• A Multi-LLM Debiasing Framework [85.17156744155915]
  Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Recent research has shown a growing interest in multi-LLM approaches, which have been demonstrated to be effective in improving the quality of reasoning. We propose a novel multi-LLM debiasing framework aimed at reducing bias in LLMs.
  arXiv  Detail & Related papers  (2024-09-20T20:24:50Z)
• Ranking Large Language Models without Ground Truth [24.751931637152524]
  Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models. We provide a novel perspective where, given a dataset of prompts, we rank them without access to any ground truth or reference responses. Applying this idea repeatedly, we propose two methods to rank LLMs.
  arXiv  Detail & Related papers  (2024-02-21T00:49:43Z)
• Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes [73.12947922129261]
  We leverage the zero-shot capabilities of large language models to reduce stereotyping. We show that self-debiasing can significantly reduce the degree of stereotyping across nine different social groups. We hope this work opens inquiry into other zero-shot techniques for bias mitigation.
  arXiv  Detail & Related papers  (2024-02-03T01:40:11Z)
• Self-Supervised Position Debiasing for Large Language Models [39.261233221850155]
  We propose a self-supervised position debiasing (SOD) framework to mitigate position bias for large language models (LLMs) Experiments on eight datasets and five tasks show that SOD consistently outperforms existing methods in mitigating three types of position biases.
  arXiv  Detail & Related papers  (2024-01-02T14:12:41Z)
• Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
  We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
  arXiv  Detail & Related papers  (2023-09-20T09:23:46Z)
• LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
  We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8% below estimated human performance.
  arXiv  Detail & Related papers  (2023-05-23T21:50:06Z)

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.