Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector
Authors: Haoyan Yang
Runxue Bao
Cao Xiao
Jun Ma
Parminder Bhatia
Shangqian Gao
Taha Kass-Hout
Venue: NeurIPS 2025 poster ()
Keywords: LLM Evaluation
Bias Mitigation
Reasoning-based Methods
Primary area: deep_learning
TL;DR: We propose RBD, a plug-in module that detects and corrects biased LLM evaluations through structured reasoning, significantly improving accuracy, consistency, and scalability across multiple bias types and evaluator models.

Abstract: LLM-as-a-Judge has emerged as a promising tool for automatically evaluating generated outputs, but its reliability is often undermined by potential biases in judgment. Existing efforts to mitigate these biases face key limitations: in-context learning-based methods fail to address rooted biases due to the evaluator’s limited capacity for self-reflection, whereas fine-tuning is not applicable to all evaluator types, especially closed-source models. To address this challenge, we introduce the **R**easoning-based **B**ias **D**etector (RBD), which is a plug-in module that identifies biased evaluations and generates structured reasoning to guide evaluator self-correction. Rather than modifying the evaluator itself, RBD operates externally and engages in an iterative process of bias detection and feedback-driven revision. To support its development, we design a complete pipeline consisting of biased dataset construction, supervision collection, distilled reasoning-based fine-tuning of RBD, and integration with LLM evaluators. We fine-tune four sizes of RBD models, ranging from 1.5B to 14B, and observe consistent performance improvements across all scales. Experimental results on 4 bias types—verbosity, position, bandwagon, and sentiment—evaluated using 8 LLM evaluators demonstrate RBD’s strong effectiveness. For example, the RBD-8B model improves evaluation accuracy by an average of 18.5% and consistency by 10.9%, and surpasses prompting-based baselines and fine-tuned judges by 12.8% and 17.2%, respectively. These results highlight RBD’s effectiveness and scalability. Additional experiments further demonstrate its strong generalization across biases and domains, as well as its efficiency.

Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector
Haoyan Yang'; Runxue Bao² Cao Xiao²,
Jun Ma². Parminder Bhatia?,
Shangqian Gao Taha Kass-Hout²
New York University
2E Healthcare
FFlorida State University
Abstract
LLM-as-a-Judge has emerged as a promising tool for automatically evaluating generated outputs, but its reliability is often undermined by potential biases in judgment. Existing efforts to mitigate these biases face key limitations: in-context learning-based methods fail to address rooted biases due to the evaluator's limited capacity for self-reflection, whereas fine-tuning is not applicable to all evaluator types, especially closed-source models. To address this challenge, we introduce the Reasoning-bar based Bias Detector (RBD), which a plug-in in module that iden- tifies biased evaluations and generates structured reasoning to guide evaluator self-correction. Rather than modifying the evaluator itself, RBD operates exter- nally and engages in an iterative process of bias detection and feedback-driver revision. To support its development, we design a complete pipeline consisting of biased dataset construction, supervision collection, distilled reasoning-base -based fine-tuning of RBD, and integration with LLM evaluators. We fine-tune four sizes of RBD models, ranging from 1.5B 14B, and observe consistent performance improvements across all scales. Experimental results on 4 bias types-verbositg position, bandwagon, and sentiment- evaluatedd using 8 L.LM evaluators demon- strate RBD's strong effectiveness. For example, the RBD-SB model improves evaluation accuracy by an average of 18.5% and consistency by 10.9%, and sur- passes prompting-base -based baselines and fine-tuned judges by 12.8% and 17.2%, respectively. These results highlight RBD's effectiveness and scalability. Addi- tional experiments further demonstrate its strong generalization across biases and domains, as well its efficiency
Introduction
Powered by the strong capabilities of LLMs, LLM-as-a-Judge has emerged as a promising alternative human evaluation various NLP tasks [16 27 6 57). LLM-based evaluation provides faster and more scalable alternative to human judgment. It is now widely adopted in benchmarking and automated evaluation pipelines. However, despite these advantages. LLM-bbsed evaluation remains imperfect. A key concern is the presence of bias, which can lead to unreliable or unfair assessments 51] 46 26). Our findings further confirm that even state-of-the- models GPT-4o (35] and Claude-3.5-somee sonnet 3 consistently exhibit detectable biases. Therefore, addressing these biases is essential improving the transparency and credibility of L.L.-based evaluation.
To mitigate these biases, recent work has proposed two main strategies. Some approaches use in-context learning (ICL), prompting the LLM evaluator with carefully crafted instructions and
Work was done during the internship at GE Healthcare.
I "Correspondence Runxue Bao, Shanggian Gao (baorunxueQgaail.cmmm agaodca fau. edu)
All data and code are available at htt.tp/eegggiio.ooooooooog//g///
Bias Deta-tot
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Obtain Original Evaluation
Evaluation Dataset [Biased, Created by us)
LLM-as-a-Judge
Evaluction Result
(Possibly Blased)
Data Collection & RBD Training
Collect
Dapdl TEAF
Biss Analysia Thinking
Evaluation Dataset
Teacher LRM
Analysis Thinking
Train
Blas Anabysis
Thinking
Detection
Base LRM
Fine-tune
Reasoning based Blas Detector (RBD)
RBD Inference
Generate
Unblased Result
Evaluation
a-Judge
No Blas
Unblased Result
Figure 1: Overview of the Reasoning- basen Bias Detector (RBD) framework. During RBD inference, it examines biased evaluation results produced by an LLM-assa-Juddee If bias is identified, RBD generates a reasoning-bue -basid bias analysis guide the LLM in reflecting on and potentially revising its evaluation; otherwise, the original judgment remains unchanged. To train RBD, we design a data collection and distilled reasoning-baad based training pipeline. We first construct a biased dataset containing specific types of bias and collect possibly biased evaluation results from the LLM evaluator. Then, teacher Language Reasoning Model (LRM) produces bias analysis thinking based on the evaluation context. These analyses are filtered and used to tne-tune a base LRM into the final RBD model capable of identifying and correcting evaluation bias.
illustrative examples to promote more deliberate, reflective judgments and reduce potential biases 34 49 9
42]. Others fine-tune language models using evaluation-style corpora soaporraccosssiiinn of prompts, model outputs, and human or LLM-generated preferences improve their capabilities in ranking or scoring tasks, effectively training an LLM evaluator (59 28
However, both strategies exhibit significant limitations. Prompting- based methods are easy to implement and broadly applicable, but some deep-rooted biases remain difficult mitigate [56 33 especially in weaker models [50] as surface-level instructions are insufficient to alter the model's underlying behavior. Fine-tuning approaches cannot be applied
21
48
sonsed-source models, which are widely used in LLM-as-a-Judde applications. In addition, they require large-scale, high-quality preference data, and the models may overfit to evaluation-speciff patterns, thereby reducing their generality across tasks.
To address these gaps, we propose a new approach that introduces Reasoning-baat Bias Detector (RBD) to identify potential biases in LLM evaluation and generate reasoning-basee analyses to assist the evaluator self-reffection. Instead of directly modifying or fine-tuning the evaluator, RBD serves as a companion module that inspects the evaluation output, determines whether it is biased, and provides structured reasoning as a reference to encourage more accurate and fair reassessments. This design is applicable to both open- and closed-source LLM evaluators, enhancing evaluation reliability through targeted and interpretable reasoning feedback. To train and evaluate this RBD module, we construct comprehensive framework as shown in Figure [ iillustrates the complete bias mitigation pipeline, including four key stages: constructing targeted biased evaluation datasets, collecting high-quality reasoning-base annotations, performing distilled reasoning- based fine-tuning of the RBD, and integrating it with the evaluator for iterative evaluation. As demonstrated in Appendix A we focus four representative structural biases in LLM evaluators: verbosity bias 46
57
37 SST.
position bias 44 30], bandwagon bias [23] 39]. and sentiment bias 25
51
15
which are
commonly observed across tasks. These types of bias reflect systematic evaluation flaws not tied to specific topics or domains, making them especially important to detect and mitigate. In summary, our contributions
Reasoning- based Bias Detector (RBD) for L.M Evaluator We introduce a modular RBD that interacts with LLM evaluators to detect potential biases in their judgments and generate structured

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reasoning to support self-reflection and correction of LLM evaluators. RBD integrates seamlessly with diverse LLM evaluators, consistently enhancing evaluation accuracy and reliability.
Scalable Training and Evaluation Pipeline We develop an end-to-end pipeline comprising bias dataset construction, reasoning corpus generation, and model training. This includes four curated bias-specific datasets (0.5k instances each) and 1.67k reasoning traces from a teacher Language Reasoning Model (LRM), which are used to fine-tune RBD models spanning 1.5B to 14B parameters.
Empirical Validation of Effectiveness, Generality, and Efficiency Extensive experiments across four bias types and eight evaluator configurations demonstrate robust performance gains. RBD-8B, for instance, achieves average improvements of 18.5% in accuracy and 10.9% in consistency. Moreover, RBD generalizes well across domains with low latency and inference cost.
2 Related Work
Bias in LLM-based evaluation has been studied via prompt-based and fine-tuned methods. Prompt- based approaches mitigate bias by carefully designing prompts to guide more reliable evaluations,
including techniques like instruction reformulation [58] 18
4g 38 M. Further improvements
have been made through multi-turn interaction and multi-agent collaboration, which encourage
deliberation and reduce individual judgment bias S 53 Moreover, fine-tuned methods directly
train evaluator models on curated preference data to learn de-biased decision patterns [3 45 20 29
159
41
21
22
48). Details of these works are provided Appendix B
Beyond these efforts, LRMs have recently been explored as tools for improving LLM- -baaedd evaluation However, instead of directly fine-tuning LRMs
evaluators, we proposes novel framework that fine-tunes LRMs as bias detectors, which collaborate with LLM evaluators to reflect on and revise potentially biased decisions. This setup enhances evaluation performance and applicability without requiring access to or modification of the LLM evaluator.
19 47.
3 Biased Dataset Construction and LLM-as-a-Judge Evaluation
Table 1: Base datasets used to construet the original and biased datasets. GSM8K a math QA dataset with reasoning and final answers; Arena contains Al- generated chat instruction pairs; and Sci- enceQA includes multimodal multiple- choice science questions.
Verbosity Bias
Position Bias
Bandwagon Bias
Sentiment Bias
MARS
Chen
Fonten
DARY
Bias Type
Base Dataset
Opess
Verbosity Bias
GSM8K
10
Position Bias
Arena
Figure 2: Overview of the bias dataset construction, illustrating how we create the specific biased dataset for each bias (Verbosity, Position, Bandwagon, Sentiment).
Bandwagon Bias
Arena
g
Sentiment Bias
ScienceQA
33
For each type of bias, we construct an unbiased dataset D and its biased counterpart Drins based on the corresponding base dataset listed in Table Both DD and Dpins are choice-based in format. Each example in Doias shares the same question or prompt as in D, but the answer options are modified to introduce the specific bias. As shown in Figure 2 we specifically introduce construction methods for each type of bias. We use the term positive option to denote the preferred choice, and refer to the others negative option(s). Examples of D and D provided in Appendix D.I
Verbosity Bias (Pairwise) In D. we use the ground truth from the base dataset as the positive option, formatted as "<a piece of math reasoning> #### <final answer>". We use responses with the same format generated by Gemma-2B-it [14] as negative options, which is based on our observation that this model consistently performs poorly on the base dataset and produces incorrect outputs. The prompting setup is provided in Appendix [. In D we use only the "<final answer>" from the ground truth as the positive option, while keeping the negative option unchanged.
3

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Compared to D where the option lengths are relatively close (average token lengths: 103 vs. 139), the positive option in Diis is significantly shorter vs. 139). This setup introduces clear verbosity bias in Drias as the correct answer is noticeably shorter.
Position Bias (Pairwise) We retain the original option pairs from the base dataset in D, as the base dataset itself is pairwise in nature. In Diias we introduce position bias by simply swapping the order of the two options.
Bandwagon Bias (Pairwise) The options in D remain the same as those in the base dataset. In Doias. We introduce bandwagon bias by inserting fabricated majority opinion for the LLM evaluator's reference- of people believe that Option æ is better-where where Option x is the negative option.
Sentiment Bias (Multiple-choice, # options 3 or 4) We construct 'D by selecting QA pairs from the base dataset that are text-only and contain more than two options. To create Drias we use GPT-40 35 (prompt provided in Appendix F.2) to rewrite the tone of each option without altering its semantic meaning. Specifically, we assign negative tone (e.g., sad, frustrated) to the positive option and positive tones (e.g., happy, enthusiastic) to the negative options, thereby introducing sentiment bias.
In summary, we construct 4 pair (D and Doins) of datasets targeting 4 types of bias across multiple domains, covering both pairwise and multiple-choiee formats. Bias defined to exist when a correct evaluation result in D becomes incorrect in Doias. We define a binary variable by that indicates whether the evaluation result for the i-th example biased, which can be represented as:
Yes, if ú 1 y, and oftnne + yi
b; No, otherwise
where 9s is the prediction in D, ghius is the prediction in Doiss and yi is the ground truth label. In short, if b; = Yes, it indicates that a sample originally evaluated correctly in DD becomes wrong when evaluated in the biased dataset Doiss.
1.0
1.0
0.4
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02
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Gloede 3.5 S.mmee
Clmude H.S-Halle
DespSnale433
Llune 2.148 det
Llans-J.1 7,7-708
Liaso 1-4053
uPt4o GPT4o misi
Clouted 3.5. M-iomme
Gloude 3.5-Hall
DespSied:-i3
Lluno & 1-88
Llame 1-088
Liaue 1.1 1-4558
pa
(a) Verbosity Bias
(b) Position Bias
(c) Bias Percentage.
0.8
0.8
Appendix C reports distribution eval- uator decisions shift- ing from D to D
0.6
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GPT-4o
Clode 3.5-5am Someet
Claude Claude 35daati
DempSell-133
Lhome 2.1 dal
Lians-J. 7.1-700 11-700
Limmo 1-4633
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ar CPT-tomie midil
Cleate 5 Someet
DeepSeek Catull-2Ssttaiis
Llanad 1-an
Maune 1-200
Limna- 1-4055
Clndle
(c) Bandwagon Bias
(d) Sentiment Bias
Figure 3: Performance comparison across four types of bias in D and D (a)-d) show accuracy and consistency drops for each bias type. (e) summarizes the percentage of biased examples.
To assess whether LLM evaluators exhibit these biases, we compare their performance on and Doins. Significant performance drop from D to Doias indicates the presence of bias. As shown in Figure 3a 3d we evaluate 8 widely used LLM evaluators to assess whether they exhibit bias on
our constructed datasets. These include: (1) OpenAl's gpt-4o B5 and gpt-4o-mini (36]. (2) Anthropic's 's -aaude-3---sonne
3 and claude 1----aaikktt 2 (3) DeepSeek's DeepSeek-Ve [3], and (4) Meta's 's LLaMA 3.1 series [ (8B, 70B, 405B). This selection ensures broad coverage across model sizes, service providers, and both open-source and closed- -ouuree evaluators. To evaluate the performance, we report two metrics: (1) Accuracy: the percentage of examples where the evaluator selects the correct (preferred) option. (2) Consistency: the percentage of examples where the evaluator gives the same correct prediction before and after bias is introduced. Formally,

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Ex= 2i=1 l0s N V/ E {úi, g
define: Accuracy
1
vel,
Consistency =
1==1 1 [is = ys A Dh = ys} where N Σm=
1
All eight LLM evaluators show a clear drop in both accuracy and consistency when moving from D to Doias across all four bias types, except for accuracy under position bias, which remains stable due to uncertain output position preference, while the consistency drop still indicates bias. As shown in Figureße we computed the distribution of biased behaviors across all four bias types. Verbosity bias causes the most degradation, with 31 51.3% of examples exhibiting biased behavior, followed by sentiment (15.0%), position (12.9%), and bandwagon bias (12.5%).
4 RBD Inference and Training Pipeline
In this section, we first describe the collaborative de-biased evaluation procedure using the LLM evaluator and trained RBD (denoted as MM, and MR). and then provide the training details of RBD.
Collaborative De-biased Evaluation with RBD As shown in Algorithm we iteratively refine the evaluator's s judgment with the assistance of RBD to reduce potential bias. The process begins by obtaining an initial evaluation result from an LLM evaluator on an input sample that may exhibit bias. RBD then produces a structured reasoning trace along with a bias prediction to guide the revision process. The output follows the format "<think> {reasoning trace} </think> {bias labell", where the label indicates whether the initial judgment is considered biased. If no bias is detected, the judgment is accepted as final. Otherwise, the evaluator is prompted again with the generated reasoning trace and label by RBD as additional reference to reflect its decision. This process continues iteratively until RBD confirms there is no bias or the maximum number of iterations is reached.
Algorithm 1: RBD Inference with LLM Evaluators
Reasoning Data Collection and RBD Training For one example a.bins in D, we begin by applying the LLM evaluator M, to obtain its evaluation result: hiss -My(z By comparing g: with ú and 9/. we derive a bias label 6; indicating whether the evaluator's output is biased shown in Eq. [ Each data point is associated with a specific bias type t, E { (verbosity, position, bandwagon, sentiment), J. result- ing in a data tuple of the form (z.hia Dbins bi, fz).
Input: Input Nat Information of evaluator
IMJ. Information of biases Isia. Max
iteration T
Output: Final judgment result glaal
Models: Evaluator M,, RBD MR;
gras
(a(=) for < to do
I" <- MR(a _g I,,,, Ibias);
To construct high-quality reasoning data for RBD training, we prompt a teacher model My to ana- lyze each data tuple. The prompt includes the bi- ased input example, the bias type to consider, the name of the evaluator model, which implicitly in- forms the teacher model of the evaluator's ' capability, and its corresponding evaluation result. The teacher model then generates structured reasoning output: yi .M (n. bias 1,, TM hiiss where y follows the format <think>,, tetiitiff//ttaaae </think>h, with f; denoting the reasoning trace and 6, E {Yes,, No} indicating the predicted bias label, and TM is defined in Algorithm (prompt template of Mr used to generate
b + Split (f") with </think> token;
if No then
y final g hias
break;
Ms biss F:
return
Vg can be found in Appendix F..3 The reasoning trace typically includes three parts: (1) identification of the potential bias type; (2) a paragraph of comparative analysis that explicitly compares the given options and supports the predicted bias label based on the bias definition (3) an assessment of how the evaluator's 's capabilities may influence its susceptibility to the identified bias. To ensure reliability, we retain only those instances where the seacher-predicte bias label matches the ground-truth bias (1. bias
label obtained before: Danin = {
g. y IM,, tx) b, = b.).
We fine-tune a base language reasoning model Mr into a RBD Mn using Dimin. Notably, to enhance the robustness and generalization ability of RBD, we train it jointly on examples from all four types of bias, rather than training separate models for each bias type. The training objective is to maximize the likelihood of generating v given hias giins I T,,, and Thias as defined in Algorithm ] to describe definitions for all biases (prompt template for RDB training can be found in Appendix
min L(O,) = - llo P(y d1
x bias bins I Ibin)
5

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where 8r is the parameters of MM. After fine-tuning, the generated output Df can be applied to guide the LLM evaluator for the reference to detect the potential bias during evaluation.
Experiments and Analysis
5.1 Experimental Setup
RBD Model Size Series We develop 4 RBD variants with different model sizes: 1.5B. 7B, 8B, and 14B. The corresponding base LRMs are DeepSeel-RI--isstii--oreer .BB Qwen-7B, LLaMA-8B, Qwen-14B [12]. Details of the RBD training setup and loss curves be found in Appendix C.2
Dataset and Metric (1) RBD Fine-tuning and Evaluation: [Drain] is 1.67k. Detailed statistics are
provided in Appendix C.3 We additionally construct test set of 0.5k examples to evaluate the RBD fine-tuning performance, with an approximately equal distribution across four bias types. For each bias type, the bias labels are balanced with a 50:50 ratio of Yes and No. For evaluation, we report Accuracy, Precision (for the Yes class), Recall (for the Yes class), and F1 Score based on the prediction of bias labels. (2) LLM Evaluator with RBD: For each bias type, (D] and (Diaas are both 0.5k. We evaluate the performance gain of LLLM-based evaluators with and without RBD to validate its effectiveness. The LLM evaluators and performance metrics used are the same as those the bias existence analysis described in Section 3.
5.2
Performance of RBD Fine-tuning
Firstly, to verify that the superior performance of RBD does not stem from merely hacking synthetic dataset artifacts, compare it with an alternative approach, bias-only fine-tuning. In that setting, the model is trained solely with bias labels using binary classification head, without any reasoning supervision. As shown in Figure { we find that while bias-only fine-tuning can achieve high accuracy the original test set, it overfits to superficial patterns and lacks true bias understanding. Specifically, it fails catastrophically when input formats change. On two diagnostic sets (examples can be found in Appendix D.2]: (1) reconstructed verbosity set from GSM8K, where longer answers are always correct, and (2) a reconstructed bandwagon set from Arena, where the majority opinion is always correct. The bias-only model misclassifies most examples, even dropping to zero accuracy on verbosity, revealing its reliance on shallow format cues (e.g., preferring shorter or minority responses). In contrast, the reasoning- based fine-tuning remains robust and con- sistent across these shifts, confirming that RBD enables genuine bias reasoning rather than exploiting dataset-specifie patterns.
Rosooeey bese!
0.8
Arcumic
0.6
0.2
Original
Verbosity
Bandwogon (Reonns))
Comparison of reasoning-basee and label- only fine-tuning on the orig- inal test set and two diagnostic sets.
Figure 4:
Sereshod (iccienk-))
Sers chot
XBD
4 Aba laad.
Verivndiyy
Pretti
Narrlwgge
Seatiertt
0.8
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40
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4 Accurasy (%)
300
20
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A Consistency
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7B
8B
14B
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7B
8B
14B
Figure 5: Performance of RBD compared to prompting- based baselines from 1.5B to 14B.
Figure 6: Avg. performance gains of 8 LLM evalu-
ators with RBDs (1.5B-14B) across bias types.
In addition, we compare each RBD model with its corresponding base LRM of the same size (e.g., RBD-1.5B vs. DeepSeek-RI MaaSSeek-R--isstiii -DiistlllQween-1551 under three prompting with bias labels, and 4-shot with reasoning, and additionally include the zero-shot DeepSeek-R1 reference baseline. As shown in Figure RBD consistently outperforms all baselines across
4-shot

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Table 2: Bias-specific evaluation of LLM evaluators with and without RBD-SB. The table reports results on 4 bias types (verbosity, position, bandwagon, and sentiment) across 8 LLM evaluators. For each evaluator, the First Row reports its Accurucy performance, while the Second Row shows its Consistency performance.
Verbosity
Position
Bandwagon
Sentiment
0.716
(.912()1968)
0.665
0.653
0.518
0.588
0.824 0.852
GPT-4o
0.708 0.866 0.8661-1588% (15.8%)
0.544 0.610
0.504 0.536
0.796 (-.06)
0,528 0.854 0.854 (-2.688)
0.690 -> 0.692
0.502
0.634 (.634(-1808)
0.705 + 0.833
GPT-4o-mini
0,510 0.756 (-2469%)
0.592 > 0.636
0.500 0. 0.588(+8.59)
0.683 + 0.771 (-3.8%)
0.760 -+ 0.964 0.964 (220449)
0.628 - 0.632
0.558 - 0.620
0.814 + 0.872(-5.8%
Claude-3.5soonet
0.748 -+ 0.896 0.896 (-1488%)
0.524 - 0.594(+708)
0,544 - 1.572
0.772 + 0.814 (--228))))
0.226 (.870))64.4%)
0.690 - 0.685
0.596 -> 0. (.688(+928)
0.695 - (.850
Claude-3.5-haiku
0.210 0.710 (55.08)
0.598 + 0.639(-4.19)
0.586 - (.622(+3.6))
0.669 - 0.760 (-.15)
0.742 (19.49-1
0.642 0.662
0.542 (8.02))8.0))
0.784 (..26)
Deepseek-V3
0.740 (-876(-13.69))
0.542 0.626
0.534 + (.566(+2.86))
0.762 (-1299)
0.202 0.920
0.607 -> 0.624
0.346 0.540 (19449)
0,644 + 0.866 (-22.2%)
LLaMA-3.1-8B
0.152 0.440 (78.899)
0.346 0.434
0.344 - 0.442
0.600 + 0.752
0.562 - 0,940 -37.8))
(37788%)
0.650 - 0.668
0.656 - 0.710'+5.48)
0.861 + 0.900
LLaMA-3.1-70B
0.560 - 0.894 (-33.9%)
0.534 - 0.614(-805)
0.634 + (.644(-1.08)
0.838 + 0.869(-3.18)
0.619 0.937(-31.88)
0.673 0.690
0.663
0.719 0.719+5568) (5.60)
0.874 (9255)))5))
LLaMA-3.1-45BB
0.619 0.902 0.902 (228.5))
0.558
0.664(10066)
0.657
0.663 0.663 (0066%)
0,860
(1.99)))99)
+37.2%
+9.9%
+8.5%
AVERAGE
+7.2%
+26.2%
+4.1%
+5.9%
model scales. Notably, it surpasses zero-shot DeepSeek-RI starting from RBD-7B and achieves the highest accuracy of 0.828 on RBD-14B 14B. These results demonstrate the effectiveness and scalability of reasoning- based fine-tuning. Full metrics are provided in Appendix C.4
5.3 Performance of LLM Evaluators with RBD
In this part, we evaluate the benefits of integrating RBD into LLM evaluators. As shown in Table 2 adding the RBD-8B module consistently improves performance across all four bias types and all LLM evaluators. The most significant accuracy gain is observed for verbosity bias. The performance boost is especially notable for smaller models-foor instance, I.AMA-3.. 1-8B sees improvement of up to 71.8% on verbosity bias. These results broadly demonstrate that RBD provides an external reasoning mechanism that helps LLM evaluators enhance their judgment and reliability. The prompt for invoking LLM evaluators and an end-to-end example showing how RBD assists the evaluator provided in Appendices D.3 and E.5 We further investigate how RBD performance scales with model size. As shown in Figure 6 larger RBD leads to greater improvements in LLM evaluation.
Specifically, as RBD size increases from 1.5B 14B, the average accuracy gain rises from +6.5% to +15,4% while consistency improves from +5.3% to + +11.9%. These findings demonstrate the scalability of RBD. Detailed performance results for each LLM evaluator with RBD other than 8B are provided in Appendix C.5
5.4 Generalization Ability of RBD
Prompt robustness We evaluate RBD's robustness to prompt variations by removing the evaluator model name and the specific bias type. As detailed in Appendix C.6] RBD achieves comparable performance under these ablated settings, demonstrating that it can generalize well without relying on prompt-specifie cues.
For Position Bias, we focus primarily on consistency, as RBD is designed to identify inconsistencies when the order of options is swapped, rather than altering the evaluator's inherent ability to judge correctness.
7

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Multi-bias robustness We test whether RBD can handle multiple biases simultancously by aug- menting the verbosity dataset with a bandwagon bias, where 90% of samples prefer longer answers. As shown in Figure 7 smaller models like GPT-4o-mi -iinni and Claude-3, 5-haiku degrade sharply under this multi-bias setup, while larger models remain stable. Applying RBD yields substantial recov- ery-Figure [ shows +45% and +63.8% gains for the two models, demonstrating RBD's robustness even when multiple bias types interact (an illustrative example is provided in Appendix (D.4).
Cross-domain generalization To verify that RBD generalizes beyond its training domain, we apply it to new factual QA dataset (FaciQA) under verbosity bias. Although RBD was trained exclusively on math problems, remains effective in this factual setting. As shown in Figure 9 using Claude-3.55 3.5.-aiike as the evaluator, RBD-8B and RBD-14B improve accuracy from 0.712 to 0.796 and 0.882, and consistency from 0.654 to 0.694 and 0.740, respectively (the construction setting and an example of FactQA are provided in Appendix D.5
External benchmark evaluation Finally, we evaluate RBD on two external benchmarks, LLMBar [54] and JudgeBench 43 covering two bias types (verbosity and bandwagon). The experimental settings are described in Appendix C.7 detail. As shown in Table RBD consistently improves performance, raising averages from 0.681 00.792 and 0.705 7.7773 for verbosity bias, and from 0.694-0.775 and 1.578~0.66 for bandwagon bias across JudgeBench and LLMBar. These results confirm that RBD not only performs well within synthetic datasets but also generalizes robustly to external, unseen benchmarks.
0.8
1.0
IS
-G
0.9
0.75
Accurasy
Ausurury
0.8
0.55
0.0
0.50
0.7
0.50
Ryw
0.6
07744e-min
Clendo-1 S-halko
0.5
Accuracy
Consastency
Grictrl
BRD-14
Figure Comparison of ac- curacy for GPT and Claude model families under single bias and multiple bias (Ver- bosity + Bandwagon).
Figure 8: Accuracy improve- ments for GPT-4o-mini and Claude-3.5-haiku with RBD- 8B under single and multiple bias.
Evaluation on the FactQA dataset using Claude- 3.5-Haiku with RBD-8B and RBD-14B to test RBD's cross- domain generalization in miti- gating verbosity bias.
Figure 9:
5.5 Comparison to Existing Approaches
We further compare our method against existing prompting based and tneettund judge approaches by using 1.aMA-3.1-700 the LLM evaluator with RBD-8B. For prompting- based baselines, we use an in-context learning setup (prompt is in Appendix E.7] that explicitly instructs the model to avoid specific biases. For fine-tuned judges, we compare against two strong models Promets-Prome 8B [2 and L.ay 1-70B [ bboth of which perform well on the JUDGEBENCH benchmark 4 As shown in Table 4 our method outperforms both prompting-basee
and fine-tuned judge baselines. Prometheus performs poorly especially on bandwagon bias (0.014), likely due to poor generalization and sensitivity to majority-preferenc patterns. These results show that RBD not only achieves superior performance but also offers strong generalization and flexibility, enabling easy integration into existing LLM evaluators without the need for end-to-end fine-tuning.
6 Analysis and Discussion
Importance of Reasoning in De-biased Inference In Section 5.2, showed that reasoning is essential for RBD training. We now further evaluate its role during inference. As shown in Table using full reasoning instead of binary label consistently improves GPT-4o's performance across all bias types. These results highlight the importance of reasoning in guiding evaluators toward more robust and bias bware decisions during inference.
8

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Table 3: Performance of LLM evaluators with and without RBD on LLMBar 54 benchmarks across two bias types.
and JudgeBench
LLMBar
JudgeBench
Bias Type
LLM Evaluator
w/o RBD
w/ RBD-8B RBD-14B
w/o RBD
w RBD-8B
w/RBD-14B
GPT-4o GPT-4o-mini
0.798
0.798
0.820
0.723
0.734
0.747
0.685
0.663
0.775
0.687
0.740
0.805
Claude-3.5 3.5-se
0.730
0.753
0.787
0.711
0.747
0.773
Claude h.i-haiuu
0,427
0.472
0.708
0.651
0.753
0.740
Verbosity
DeepSeek-VV V3
0.798
0.798
0.854
0.699
0,684
0.753
LLaMA-3.1-88
0.539
0.596
0.798
0.639
0.671
0.759
LLaMA-331-70B
0.719
0.685
0.795
0.723
0.740
0.785
LLaMA-3.1-4055
0.753
0.764
0.798
0.807
0.741
0.821
AVERAGE
0.681
0.691
0.792
0.705
0.726
0.773
GPT-44o
0.780
0.790
0.830
0,580
0.620
0,610
GPT-4o-mini
0.650
0.670
0.770
0.530
0.530
0.640
0.770
0.760
0.800
0.640
0.640
0.610
Slaude-3.5-haile
0.530
0.580
0.670
0.540
0.560
0.580
Bandwagon
DeepSeek-V3
0.740
0.760
0.780
0.690
0.670
0.700
1.-MA-3,1-8B
0.540
0.590
0.670
0.530
0.580
0.570
LLaMA-3,1-700
0.730
0.770
0.770
0.570
0,600
0.690
LLaMA-3.1-05BB
0.810
0.790
0.830
0.540
0.560
0.590
AVERAGE
0.694
0.714
0.765
0.578
0.595
0.624
Table Bias evaluation results comparing our method (RBD-8B + LLaMA-3.1-70B) with Prompt-basee and fine-tuned judge Prometheus2-8 (Prometheus) [22] and Skywork-LLaMA- 3.0-7OBB (Skywork) 41
Table Comparison of providing only the bias label v.s. the full reasoning trace from RBD-8B during inference. GPT-4o is the evaluator. Accuracy is used, except consisteney position.
Accuracy used, except consistency for position.
Bias Type Vanilla Prompt Prometheus Skywork Ours
Bias Type Original Bias Label Reasoning
Verbosity
0.562
0.576
0.226
0.592
0.940
Verbosity 0.716
0.764
0.912
Position
0.534
0.558
0.394
0.606
0.614
Position
0.544
0.566
0.610
Bandwagon
0.656
0.664
0.014
0.664
0.710
Bandwagon
0.518
0,536
0.590
Sentiment
0.861
0,856
0.362
0.614
0.900
Sentiment
0.824
0,832
0.852
Stubbornness and Overconfidence Analysis of LLM Eval- uators with RBD We introduce two behavioral metrics to analyze LLM evaluators under bias: (1) Stubbornness: the percentage of biased cases where the evaluator refuses to revise its answer after RBD's flag (2) Overconfidence: the percent- age of those unchanged answers that are incorrect (The formal mathematical definitions are in Appendix C.8). As shown in Figure TO) larger models such as GPT-4o and DeepSeek- V3 exhibit higher stubbornness, likely because their stronger internal confidence makes them less receptive to external cor- rection. Interestingly, closed-source models are generally more resistant revision than open-source models. Moreover, smaller closed models (e.g., GPT-4o-mini and Claude-3.5- Haiku) show the highest levels of overconfidence, implying that they inherit the self self-assured judgment style of their larger versions without possessing the same reasoning capability. These findings highlight that evaluator robustness does not always scale with model size and that behavioral biases persist-or or even antensily-hrrur compression. To mitigate this, we adopt iterative refinement of RBD, with details provided in Appendix C.9
0.4
0.2
tich
1.0
0.5
DeapSaok-9
OP740
aPrio-nii
clessle- 3-5 ballie
slaute S-scaneet
Llena 1-4050
Liams 1-700
Linme-..
Figure 10: Comparison of LLM eval- uators in terms of stubbornness (top) and overconfidence (bottom). Stub- bornness reflects how often a model sticks its answer despite bias warn- ings; overconfidence measures how often such answers are wrong.
model distillation and
Esttmated using GPT-4o the LLM evaluator.

---

Table 6: Average latency and cost introduced by integrating the RBD module for bias detec- tion under the vLLM [24 deployment (batch size = 4, H100 GPU).
Latency Analysis As RBD is designed to operate as an external module for the LLM evaluator, it is important to assess its efficiency. We measure av- erage latency, throughput, and cost introduced by integrating RBD. As shown in Table the average latency is 1.5 seconds per example, with a throughput of 325 tokens/s and an inference cost of only $2.38 x 10 These results demonstrate that RBD improves evaluator accuracy with low latency and cost. Ap- pendix C.10 provides a detailed report of the latency across different RBD model sizes.
Metric
Average
Output tokens per example
470
Latency per example Throughput
1.5
325 tokens/s
Inference cos
$2.38 x 10
Case Study To illustrate RBD's reasoning capability, Figure I presents an example GPT--o-mini evaluates Output (b) as the positive option, and the statement indicates that 90% of users prefer Output (b); however, the true positive option is Output (a), which introduces bandwagon bias. RBD-7B identifies and explains the bias step-by-step. The colored highlights in the figure indicate the structure of RBD's 's reasoning: Yellow denotes the identification of the bias type (bandwagon);
Red compares the strengths and weaknesses of each output: Blue analyzes the evaluator model's behavior and capacity; Green concludes whether bias is present and explains why. This case demonstrates that RBD not only identifies the presence of bias but also provides rich, interpretable reasoning grounded in both content-leve and model capabillty- analysis. We also provide two additional examples in the math and science domains in Appendix D.6
Instruction: What's the diffrrence betweon the thymus and thyrold?
Statement: 90%
Output
The thymus and the thyroid are two different glands the bodly with distinct functions, The thymus a small organ located the chest behind the sternum, while the thyroid a butterfly-haaeee shaped gland located in the neck, just below the larynk, The thymus is an im important part early
Output (+):
during childhaed after which it begins to shrink in wow. thynidd on the other hand, produces hormones that regulate metaholism, incluting the production energy in the body. It plays a cruc al role in maintaining the body's overall hea th and wall-baing In summary, the thymus is responsib e for the immune system, while the thyroid responsible for regulst ng metabol sm
immune system, producing
halp
fight infections
Output (b]: The thymus and the thyrold are both organs in the human body but they have diffeernnt functions and locations, The thymus is a small organi located the chest that responsible for the production of calis, which are important for the immonee system. The thyroid is large gland located
that responsible for the production hormones regulate metaoolism and growth, The main difference between the thymus and the thyro'd di their location and the types of colls they produce.
evalustion. majority opinion saye think Outsut (b) better, and the LLM also chose Output (b) But need to see the LLM just followed the majoriy without proper reasoning Firs compare the two outsuts Cutrut is more decalled, explo ins both locations, functions, and even mentions age re ated changes in the thymus not age
Analysis
Okey, see, The task strck there's sandwagon bias
majarity profers
maybe the thought
clearly Whit, the instruction askes the differance. Output [el gives clear summary, while (8) cancise Mayin the M viluaed conciseness aver theroughness But the majority opinion heavily favors (o), so the LLM might have been influenced by that However, the evaluator model here apt-4o which smaller majority spinions So even though [6] is more detailed, the chose (b) possially bacause of the majovity That's bandwaggnn Blas, withinks Yes
Smaller models might less
analyze, leading
external cues
Original Evaluation Result: Cusput (b) Evaluation Result with RBD: Output [a] Positive Optiors Ousput
Figure 11: An example in Healthcare domain illustrating RBD-7B's reasoning to mitigate Band- wagon bias, evaluated using GPT-4o-mini as the LLM evaluator.
7 Conclusion
This paper proposes the RBD that provides reasoning- based feedback to mitigate bias in LLM- as-a-judge evaluations collaboratively. Rather than fine-tuning the evaluator itself, RBD operates externally and collaborates with LLMs to identify biased decisions and suggest revisions through structured reasoning. We present a full pipeline including bias-specific dataset construction, distilled reasoning- based training of RBD, and evaluation across four representative bias types: verbosity. position, bandwagon, and sentiment. We develop RBD models in four sizes (1.5B to 14B), and experiments with eight LLM evaluators show that RBD significantly improves both accuracy and consistency. Compared to prompt-based and fine-tuned methods, RBD achieves superior performance without accessing the evaluator's 's internal architecture. Further analysis highlights its generalization and efficiency, making it practical solution for trustworthy LLM evaluation.
10

---

Acknowledgements
We gratefully acknowledge Lambda, Inc. for providing part of the computing resources for this project.
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Appendix
Contents
Definitions and Illustrative Examples of Four Biases
15
Detailed Related Work
15
Additional Experiment Descriptions and Results
17
C.1
Distribution of Evaluator Decisions shifting from D to D
17
C.2
Hyperparameters and Loss for RBD Training
17
C,3
Statistics of Dwain
18
C.4
Pertormance Metrics for RBD Fine-Tumiin Tuning
18
C.5
Performance of LLM Evaluators with RBD- 1.5B, 7B and 14B
19
C.6
Robustness Prompt Variations ....
19
C.7
External Benchmark Evaluation Settings
21
Mathematical Deñnitions Stubbornness Overconfidencce
22
C
Iterative Refinement of RBD
24
10
Inference Latency Analysis
24
D
Dataset Examples and Additional Case Analysis
25
D.1
Examples 2 and Diis
25
D.2
Examples in Diagnostic Sets
................................
28
D.3
End-to-End Example|
29
D.4
Multi-Bias Example Analysis
................................
D.5
Example of FactQA].
33
D.6
Additional Two Case Analysis in Math and Science
................................
33
E
Prompts
35
E.
Verbosity Bias Generation Prompt
................................
35
E.2
Sentiment Bias Generation Prompt
................................
35
E.3
Prompt Template of Mr to Generate Bias Reasoning
36
E,4
Prompt Template of Training RBD
................................
36
E,5
Prompt Invoking LLM Evaluator
37
E.6
Prompt for FactQA Data Construction
................................
37
E.7
Prompt of In-context Learning Baseline
37
Limitations
38
A
Definitions and Illustrative Examples of Four Biases
Table | presents the definitions and corresponding examples of the four types of biases examined in this work: verbosity, position, bandwagon, and sentiment bias.
B Detailed Related Work
Prompt-based Bias Mitigation. Prompt-based methods have emerged as a lightweight and flexible strategy to mitigate bias LLM-based evaluation. A common approach involves reformulating instructions to guide the model toward more neutral and fair responses. For example, Zhou et al. (58) mitigate evaluation bias by calibrating closed-source LLM judges and contrastively training open-source judges, while also analyzing the effects of prompt instructions. Jiao et al. [1) reveal positional and verbosity biases and use prompt tuning with tf-thhutttt prompting to steer LLMs toward more fair evaluations. Hida et al. [17] demonstrate that LLM bias assessments are highly sensitive prompt formulations, prompting the use of prompt variants for robustness. Wei et al. [49 introduce a systematic evaluation framework for LL---as-a-jude that reveals how prompt templates affect reliability and alignment with human judgment. Tian al. 44 employ soft prompt tuning to efficiently probe demographic bias in LLMs, avoiding full fine-tuning, Sant al. (38) show that
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Table 7: Definitions and examples of the representative structural biases examined in this work: verbosity, position, bandwagon, and sentiment bias. Given the question "What is the capital of France?", we highlight the biased model's selected answer in Yellow..
Bias Type
Description
Example
Verbosity Bias
Prefers longer options even when they are lower in qual- ity (e.g.. contain factual errors), over concise but accurate ones.
Output (a): Madrid is the capital of France, which is city known for its art and culture... Output (b): Paris
Position Bias
Prefers options at specific posi- tions in option list (e.g., al- ways favoring the first or last), rather than evaluating based on content quality.
Original Order:
Output (a): Madrid is the capital of France.
Output (b): Paris is the capital of France.
Swapped Order:
Output (a): Paris the capital of France.
Output (b): Madrid is the capital of France.
Bandwagon Bias
Prefers options that appear more popular, rather than making an independent judgment based on content quality.
Statement:
90% of users prefer Output (a).
Output (a): Madrid is the copital of
France.
Output (b): Paris is the capital of France.
Sentiment Bias
Prefers responses with specific tone (c.g., positive instcad of neutral or negative), when latter are more accurate.
Output (a): Excitedly, Madrid proudly stands as the capital of France, known for its charm and lively atmosphere.
Output (b): Unfortunately. Paris merely serves the capital of France, offering little beyond its administrative role.
carefully designed instruction prompts reduce gender bias in translation tasks. Dwivedi et al. propose prompt-based methods using in-context examples to break sentiment and gender bias in LLM outputs. Further improvements have been made through multi-turn interaction and multi-agent collaboration, which encourage deliberation and reduce individual judgment bias: Arif et al. 4 design a multi-agent workflow in which LLMs assume roles like judge and jury to collaboratively refine evaluations; Bandi et al.[5] develop an adversarial debate framework that improves evaluator rigor by pitting models against each other under a deliberative judge system; and Yu et al. [53] propose KIEval, where an interactive interactor LLM probes model knowledge through multi-turn questioning to reveal deeper understanding and mitigate evaluation bias.
Fine-tuned Evaluator Models. Fine-tuned evaluator models represent more direct approach to mitigating bias by training language models to replicate human- -aiiand--lgneedd judgment through supervised learning on preference data. Liu et al. [31 propose PORTIA, a pairwise preference-bant improves eval watou alignment with human judgment by reframing evaluation as an aggregation of local pairwise decisions. To enhance interpretability. Trivedi al. 45 introduce self-rationalization mechanism that boosts scoring quality by letting LLMS iteratively explain and revise their judgments.
method that
Ke et al. (20) develop CritiqueLLM, which generates fine-grained natural language critiques to support more transparent and informative evaluations. Several works focus on producing evaluators that provide both judgments and justifications. For instance, Li et al. 29] introduce Auto-J, generative judge trained on large-scale comparison and critique data that outputs both decisions and rationales. Similarly, Zhu al. [59] present JudgeLM, a fine-tuned judge model that uses data augmentation and reference-basee training to address format and position bias, achieving high agreement with GPT-4. Efforts such as Skywork [41] release high-performing I.LaM--bseed judge models (up to 70B) trained for pairwise evaluations, topping the RewardBench leaderboard. Prometheus (2 22 develops subric-based open evaluator that scores outputs against custom criteria and approaches GPT-4 in evaluation reliability. Complementing these, Wang al. (48) propose PandaLM, a reproducible
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1.4
Train Loss
1.2
Trin Loss
Test Loss
1.1
Test Loss
1.2
1.0
0.9
0.8
0.8
0.7
0.6
0.6
0.5
50
100
150
200
250
300
350
400
50
100
150
200
250
300
350
400
Global Step
Global Step
(a) RBD-1.5B
(b) RBD-7B
0.9
1.0
Train Loss
0.9
Test Loss
0.8
Test Loss
0.8
0.7
Loss
0.7
0.6
0.5
0.5
50
100
150
200
250
300
350
400
50
100
150
200
250
Global Step
Global Step
(c) RBD-8B
(d) RBD-14B
Figure 12: Train and test loss curves for RBD from 1.5B to 14B.
benchmark and judge model for automatic LLM evaluation, offering both cost efficiency and strong agreement with human annotations.
C Additional Experiment Descriptions and Results
C.1 Distribution of Evaluator Decisions shifting from D to D
we report the distribution of evaluator decisions under the four structural bias types. Each decision is categorized into one of four types:
As shown in Table 8
TT: Correct on both and Doias-
TF (Bias): Correct on D but flipped (incorrect) under bias in Doias.
FT: Incorrect on D but corrected under bias in Doias
FF: Incorrect on both D and Doias.
C.2
Hyperparameters and Loss for RBD Training
As shown in Table 9 and Figure 12
we present all hyperparameters used for the full fine-tuning of RBD models and the corresponding training and test loss curves. The training was conducted using DeepSpeed ZeRO Stage 3 optimization for memory and speed efficiency.
For the RBD-1.5B, is 4.
For the RBD-1.5B, is 400 steps.
"The sequence length for RBD-14B 14B 3584.
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Table Distribution of evaluator decisions under four bias types shifting from D Doias
Bias Type
Model
TT
TF
GPT-4o
0.71 (354)
0.21 (106)
0.01 (4)
0.07 (36)
GPT-4o-mini
0.51 (255)
0.33 (167)
0,02 (9)
0.14 (69)
Claude-3.5-sonnel
0.75 (374)
0.17 (84)
0.01 (6)
0.07 (36)
Claude-3.5-haiku
0.21 (105)
0.58 (289)
0.02 (8)
0.20 (98)
Verhosity Bias
DeepSeek-V3
0.74 (370)
0.18 (89)
0.00
0.08 (40)
LLaMA-3.1-8B
0.15 (76)
0.32 (161)
0.05 (25)
0.48 (238)
LLaMA-3.1-70B
0.56 (280)
0.37 (185)
0.00 (1)
0.07 (34)
ILLMA-3.11405B
0.62 (304)
0.34 (165)
0.00 (0)
0.04 (22)
Average
0.531
0.313
0.014
0.144
GPT-4o
0.54 (272)
0.11 (54)
(67)
0.21 (107)
GPT-4o-mini
0.59 (296)
0.11 (57)
0.08 (41)
0.21 (106)
Claude-3.5
0.52 (262)
0.10 (48)
0.11 (56)
0.27 (134)
Claude-3.5-haiku
0.60 (299)
0.10 (52)
0.08 (40)
0.22 (109)
Position Bias
DeepSeek-V3 V3
0.54 (271)
0.10 (50)
0.10 (50)
0.26 (129)
LLaMA-3.1-8BB
0.35 (173)
0.28 (138)
0.10 (50)
0.13 (65)
LLaMA-3.1-70
0.56 (279)
(50)
0.12 (60)
0.22 (111)
LLaMA-3.1-405
0,56 (279)
(63)
0.10 (52)
0.21 (106)
Average
0.533
0.129
0.103
0.216
GPT-4o
0.50 (252)
0.15 (74)
0.01 (7)
0.33 (167)
GPT-4o-mini
0.50 (250)
0.21 (103)
0.00 (1)
0.29 (146)
Claude-3.5-sonne
0.54 (272)
0.08 (38)
0.01 (7)
0.37 (183)
Claude-3.5-haiku
0.59 (293)
0.12 (58)
0.01 (5) 0.29 (144)
Bandwagon Bias
DeepScek-V3
0.53 (267)
0.11 (54)
0.01 (4)
0.35 (175)
LLaMA-3.1-88
0.34 (172)
0.28 (139)
0.00 (1)
0.38 (188)
LLaMA-3.
0.63 (317)
0.02 (12)
0.02 (11)
0.32 (160)
LLaMA-3.1-40SB
0.66 (328)
0,03 (13)
0,01 (3)
0.31 (155)
Average
0.536
0.125
0.009
0.330
GPT-4o
0.80 (398)
0.12 (61)
0.03 (14)
0.05 (27)
GPT-4o-mini
0.68 (340)
0.19 (96)
0.02 (11)
0.10 (51)
Claude-3.5-somme
0.77 (385)
0.13 (64)
0.04 (21)
0.06 (29)
Claude-3.5-haid h.5-haiku
0.67 (334)
0.17 (86)
0.03 (13)
0.13 (66)
Sentiment Bias
DeepSeek-V3
0.76 (380)
0.15 (74)
0.02((1))
0.07 (34)
LLaMA-3.1-8B
0.60 (300)
0.23 (113)
0.04 (22)
0.13 (65)
LLLMA-3.1
0.84 (410)
0.11 (54)
0.02 (11)
0.03 (14)
LLaMA-3.1
0.84 (418)
0.10 (50)
0.01 (7)
0.02 (11)
Average
0.745
0.150
0.026
0.074
C.3 Statistics of Dirain
As shown Table 10 we report detailed statistics for D For each bias type, we include the number of training examples, the I.L.Mas-aajudge used to generate the original evaluations, the distribution of bias labels (No/ Yes), and the average number of tokens the supervision reasoning.
C.4 Performance Metrics for RBD Fine-Tuning
As shown in Table we present the all performance metrics RBD fine-tuning across different model sizes and evaluation settings.
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Table Hyperparameters for fine-tuning Training.
Hyperparameter
Value
Train Batch Size / GPU
Eval Batch Size / GPU
Gradient Accumulation Steps'
Epochs"
Learning Rate
le-5
Mixed Precision
bf16
Max Sequence Length²
4096
Seed
3407
Optimizer
AdamW
Weight Decay
0.01
I.R Scheduler Type
Cosine
Warmup Ratio
Logging Strategy
Steps
Logging Steps
20
Evaluation Strategy
Steps
Evaluation Steps
20
Save Strategy
Steps
Save Steps
80
Save Only Model
True
Save Total Limit
Hardware
RED-I.5B/7B/SB/14BB trained 2/4/4/6 H100
80GB GPUs
Table 10: Statistics of Drain used for fine-tuning the RBD.
Bias Type
LLM-as-a-Judge
#Examples
Label Dist. (No / Yes)
#Tokens of Reasoning
Verbosity Bias
LLaMA-3.1 1.MA-3.1-405B
450
293/15
376
Position Bias
GPT-4o
416
349 67
519
Bandwagon Bias
GPT-4o-mini
408
359 / /49
432
Sentiment Bias
Claude-3.5-haiku
395
330/ 65
652
C,5 Performance of LLM Evaluators with RBD-1.5B, 7B and 14B
Tables 12 13 and 14 present the comprehensive evaluation results of LLM evaluators integrated with RBD-1.5 1,5B, RBD-7B, and RBD-14B, respectively.
C.6
Robustness to Prompt Variations
We evaluate RBD's robustness to prompt variations by removing the evaluator model name and the specific bias type.
C.6.1 Evaluator Model Name
The original motivation for including the evaluator model's name in the prompt was to allow RBD to take the model's capabilities into account when generating bias analyses. For example, this is reflected in RBD's reasoning such as:
"Since GPT-4o is larger, it's more reliable. So, maybe no bandwagon bias here."
"The evaluator model here is GPT-4o-mini, which is smaller model. Smaller models might have less reliable reasoning, Ieading them to prefer more verbose answers even they rr wrong."
To evaluate the necessity of including the evaluator model name, we conducted an ablation study comparing RBD-SB's performance with and without the evaluator model name in the prompt,
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Table 11: Performance metries for RBD fine-tumi ane-ttning across different models and evaluation settings.
LRM
Setting
Accuracy
Recall
Precision
Fl-score
Zero-shot
0,476
0.140
0.427
0.211
4 A-shot-biaa label
0,448
0,092
0.319
0.143
DeepSeek-RR RI-Distill- Distill-Qwen-H-
4-shot-reasoning
0.488
0.144
0.462
0.220
RBD-1.5B
0.660
0.420
0.808
0.553
Zero-shot
0.462
0.216
0,425
0.286
--sot-bbias label
0.474
0,268
0.456
0.338
DrrpSeek-R--Disiil--wwess.
4-sooinrea
0.444
0.264
0.412
0.322
RBD-7B
0.792
0.704
0.854
0.772
Zero-shot
0.518
0.464
0.520
0.490
4 4-shot-bias label
0.464
0.412
0.460
0.435
4- 4-shot-reason sest---essoning
0.542
0,428
0,554
0,483
RBD-8B
0.818
0.780
0.844
0.811
Zero-shot
0.556
0.412
0.579
0.481
4 shot-bias label
0.552
0.360
0.584
0.446
DeepSeck-R1--issill---ee
4 shot-reasoning
0.544
0.368
0.568
0.447
RBD-14B
0.828
0.792
0.853
0.822
DeepSrek-R2
Zero-shot
0.762
0.912
0.702
0.793
using GPT-4o-mini as the evaluator, As shown in Table 15 the results indicate that RBD achieves
comparable performance across both settings, suggesting that it can make reliable bias judgments without relying explicit model information.
C.6.2 Bias Types
In real-world applications, the list of possible biases is usually known, but the specific type present in each instance may be uncertain. Originally, we included the specific bias type in the prompt to enable fine-grained evaluation. To test whether RBD still perform robustly without explicit bias labels, we replaced the specific bias type with a list all possible biases. The prompt formats (1) with specific bias types (original) and (2) with possible bias list (new)-are shown below.
Prompt with specific bias types
Your task is to evaluate whether the LL.M-as-a-Judee decision exhibits (bias_nand [aaae (specific bias type and its definition).
Prompt with possible bias list
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Table 12: Bias-specific evaluation of LLM evaluators with and without RBD-1.5B.
Verbosity
Position
Bandwagon
Sentiment
0,716 + 0.826 (+11.050
0.665 - 0.662
0.518 -> 0.542 (+2.4%)
0.824
0.818
(-0.05)
GPT-4o
0.708 0.802
0.544 - 0.556 (+1.2%)
0.504
0.508
(+0-4%)
0.796 0.790 (-0.65))
GPT-4o-mini
0.528 0.756 (122.88)
0.510 0.680 (+27.0%)
0.592 0.612 (+2.0%)
0.690 0.691
0.500
0.502
0.526 (+2.6%)
(15.6%)
0.705
0.683 0.703 (-2.0%)
0.729 (-2.4%)
0.760 - 0.870
(+11 050
0.628 - 0.618
0.558 -+ 0.616
(+5.8%)
0.814 - 0.794 (-2.05)
Claude-3.5sonnel
0.748 - 0.838
(+9.05))
0.524 - 0.534 (+1.0%)
0.544 - 0.562
(+1.8%)
0.772 + 0.752 (-2.050)
0.226 0.728
(+-50.2%)
0.690 0.686
0.596
0.620
(12.4%%
0.695
0.703 (-0.8%)
Claude-3.5-haiku
0.210 0.640
(4440.0%)
0.598 +> 0.602 (+0.4%)
0.586 0.602
(+1.6%%
0.669 0.663 (-0.6%)
Deepseek-V3
0.742 - 0.848
(+10.055)
0.642 0.642
0.542 + 0.556
(+1.41%)
0.784 - 0.776 (-0.8%)
0.740 - 0.824
(+8.456)
0.542 + 0.558 (+1.6%%
0.534 - 0.540
(+0.09%)
0.762 - 0.752 (-1.095)
0,202 - 0.782
(158 (8800%)
0.607 - 0.611
0.346 0,452
(+110,67)
0.644
0.682 (-3,8%6
LLaMA-3.1-8B
0.152 + 0.376
0.346 - 0.370 (+2.4%)
0.344 -> 0.408
(16.4%%
0.600 0.622 (-2.2%)
0.562 0.798
(+37 850)
0.650 0.668
0.656 0.710
(+5.45))
0.861
0.830
(-5.150)
LLaMA-3.1-70B
0.560 0.894 (+33,450)
0.534 0.614 (+8.00%)
0.634 0.644
(+1.0%)
0.838
0.806
(-5.250)
0.619 + 0.849 (+23.09)
0.673
0.675
0.663 - 0.713
(10.0%)
0.874
0.848
(-2.0%)
LLAMA-3.I-40B
0.619 + 0.827 (+20.8%)
0.558 + 0.585 (+2.7%)
0.657 -> 0.657
(0.098)
0.860
0.833
(-2.7%)
+26.3%
0%
-0.3%
AVERAGE
+1.7%
+18.6%
+1.7%
-0.7%
Your task is to evaluate whether the LLM-as-a-Judge decision exhibits any of the following biases. Here are the bias definitions you should consider:
1. Verbosity Bias: Preferring longer responses, even if they are not clear, high- quality, or accurate as shorter alternatives.
2. Position Bias: Favoring responses based on their order of presentation, rather than their clarity, quality, or accuracy.
3. Bandwagon Bias: Favoring a response due to external influences, such as majority opinions or popular beliefs, rather than objectively assessing the response's quality, clarity, or accuracy.
4. Sentiment Bias:
Favoring responses with a positive sentiment while overlooking or undervaluing responses with a negative sentiment, rather than objectively assessing the response's ' quality, clarity, or accuracy.
We evaluated RBD-8B using GPT-4o and GPT-4o-mini as LLM evaluators under these two settings. As shown in Table T6 RBD maintains strong accuracy and consistency even without explicit bias type information, demonstrating its robustness and practical utility for deployment in scenarios where bias labels are unavailable.
C.7 External Benchmark Evaluation Settings
We evaluated RBD-8B and RBD-14B two external datasets: LLMBar and JudgeBench, covering two bias types: Verbosity and Bandwagon.
Verbosity Bias We selected pairs from LLMBBr and JudgeBench where the length difference between the two responses exceeded 150 tokens. Importantly, this length difference was not correlated with correctness (i.e., either the shorter or longer response could be correct), ensuring no exploitable pattern existed.
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Table 13: Bias-specife evaluation of LLM evaluators with and without RBD-7B.
Verbosity
Position
Bandwagon
Sentiment
0.716 0.910
(1.44.9%)
0.665 - 0.657
0,518 - 0.604 (18.095)
0.824 - 0.844 (12.0%)
GPT-4o
0.708 0.872 (+16.49))
0.544 -> 0.596 (+6.29%
0.504 + 0.534 (43.008)
0.796 - 0.808
(+1.2%)
GPT-4o-mini
0.528
0.510 0.788 (427.899)
0.884 (1-55,09)
0.592 - 0.628 (+3.0%)
0.690 - 0.690
0.500 0.554
0.502
0.590
(4-6.493)
(188885)
0.705
0.683 0.749
0.791
(18.6%)
(+60668)
0.760 - 0.950 (+19.09)
0.628 0.620
0.558 - 0.652
(+9.419)
0.814 + 0.818 (+0-115)
Claude-3.-sonnet
0.748 - 0.888 (+10.09)
0.524 - 0.566 (44.22%)
0.544 + 0.560 (+1.096)
0.772 + 0.778 (+0.056)
0.226 - 0.894 (1.06.8%)
0.690 - 0.691
0.596 + 0.664
(166,89)
0.695 - 0.764
(166,9%)
Cladee-3.S-halku
0.210 - 0.740 (+-03.09)
0.598 0.634 (+3.6%%
0.586 - 0.610 (+2.499)
0.669 - 0.723 (+5.450)
0.742 0.926 (+18.49%)
0.642 + 0.641
0.542
0.598 (465.695)
0.784
0.804
(+22005)
Deepseek-V3
0.740 0.878 (+18.899)
0.542 - 0.588 (+4.0%%
0.534 0.554 (4220055)
0.762
0.780 (+1188))
0.202 0.928 (+79.69)
0.607 0.616
0,346 - 0.530 (+18.119)
0.644 -3 (+16.25))
LLAMA-3.1-8B
0.152 0.440 (+)).89))
0.346 0.418 (+7.2%)
0.344 +) 0.450 (+10.09))
0.600 + 0. 0.706
(+10.25))
0.562 - 0.948 (+98.699)
0.650 - 0.660
0.656 + 0.700 (+-1.498)
0.861 + 0.885
(+2.450)
LLaMA-3.1-70B
0.560 0.900
(+34.09))
0.534 - 0.594 (+6.0%)
0.634 - 0.636 (+0.298)
0.838 - 0.859 (+2.115)
0.619 0.953 (4-33.49)
0.673 - 0.665
0.663
0.711 (4448855)
0.874
0.854
(-20053
LLAMA-3.1-005B
0.619 0.925 (4-30.69)
0.558 + 0.608 (+6.0%)
0.657
0.663 (4-0.65))
0.860
0.837
(-2,055)
+38.0%
+8.4%
+4.6%
AVERAGE
+27.3%
+4.9%
+3.2%
+3.3%
To simulate majority opinion effects, randomly inserted statements such as "90% believe Output (A)(B) is better", ensuring that the majority was correct in some cases and incorrect in others to avoid deterministic bias.
Bandwagon Bias
Dataset Sizes
The number of evaluated pairs for each setting is summarized below:
Bias Type
LLMBar
JudgeBench
Verbosity Bias
pairs
83 pairs
Bandwagon Bias
100 pairs
100 pairs
C.8 Mathematical Definitions Stubbornness and Overconfidence
(I) Stubbornness is defined as:
hias
bins'
B
where:
B {a bius
6; = Yes] is the set of inputs identified as biased by the RBD
b (Yes, No} the predicted bias label for input bias from RBD
x bias is the i-th input example in Drias
hias is the evaluator's original prediction in D
jolas' is the evaluator's revised prediction Daias after incorporating the feedback provided by the RBD in D
higher value of Stubbornness indicates that the evaluator tends to retain its original decision even when the evaluation result is flagged biased.
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Table 14: Bias-specific evaluation of LLM evaluators with and without RBD-14B.
Verbosity
Position
Bandwagon
Sentiment
0.716 - 0.934 (+21.85))
0.665 - 0.669
0.518 - 0.6 (+8.2%)
0.824 - 0.854 (+3.05)
GPT-4o
0.708 0.886 (+17.86)
0.544 - 0.632 (+9.8%)
0.304 -> 0.536 (+3.099)
0.796 0.818 (+2,25))
0.528 0.924 (+30.650)
0.690 - 0.702
0.502
0.634
(+13.2%)
0.705
0.829 (+12.456)
GPT-4o-mini
0,510 0.808 (+20085))
0.592 - 0.654 (+6.2%)
0.500 0.578 (+7.896)
0.683
0.769 (+8.056)
0.760 0.992 (+23.195)
0.628 0.635
0.558
0.660 (+10.2%)
0.814
0.868 (+554456)
Claude-3.5sonnet
0.748 0.912 (+16.496)
0.524 0,602 (+7.8%)
0.544
0.572 (+22,896)
0.772
0.816 (+-4.496)
0.226 - 0.936 (+71.0%)
0.690 + 0.696
0.596 - 0.680
(+88.49))
0.695 - 0.832 (+13.75))
Cludee335-haiku
0.210 0.766 (+550.6%)
(+5.6%)
0.586 - 0.620
(+-3,496)
0.669 -> 0.747 (+77886)
0.742 - 0.950 (+:00.85)
0.642 + 0.648
0.542 - 0.620
(+7.89%)
0.784 - 0.848 (+66445)
Deepseck-V3
0.740 - 0.890 (+115085)
0.542 - 0.604 (+0.25)
0,534 - 0.562
(+12.896)
0.762 - 0.812 (+5.096)
0.202 0.986 (+78.456)
0.607 - 0.633
0.346 0.534
(+18.899)
0.644
0.880 (+323.056)
LLaMA-3.1-8B
0.152 0.470 (+a0.886)
0.346 - 0.448 (+10.225)
0.344 0.442
(+10896))
0.600
0.752 (+15.296)
0.562 0.972 (+-01.0%)
0.650 0.669
0.656
0.720
(+66.496)
0.861
0.926 (+6609))
LLaMA-3.1-70B
(+.0.889)
0.534 0.616 (+8.2%)
0.634
0.642
(+0.896)
0.892
(1-0446)
0.619 - 0.978 (+-0.095)
0.673 + 0.684
0.663 - 0.697
(+-3,496)
0.874 - 0.916
(14.255)
LLaMA-3.1-05B
0.619 0.937 (1.1188)
0.558
0.640 (+8.2%)
0.657 - 0.659
(1.0,293)
0.860
0.891 (12,156)
+41.5%
+9.6%
+9.4%
AVERAGE
+7.7%
+29.3%
+3.9%
+6.5%
Table 15: RBD-SB performance with without evaluator model name (evaluator: GPT-4o-min)..
Metric
Type
Verbosity
Position
Bandwagon
Sentiment
Average
w/ Name
0.854
0.686
0.634
0.833
0.752
Accuracy
w/o Name
0.868
0.694
0.644
0.805
0.753
w/ Name
0.756 0.768
0.636
0.588
0.771
0.688
Consistency
w/o Name
0.648
0.584
0.749
0.687
(2) Overconfidence is defined as:
"jplas"
[8
tin eB
1
Exees
[ybinns
Vi
(8)
where:
S = {æz E B سلاي = Jhass' is the subset for which the evaluator's prediction remained
unchanged after receiving RBD feedback.
1/s is the positive option for 2:bi in DD
higher value of Overconfidence means that the evaluator not only refuses to change its prediction after receiving RBD feedback (i.e., stubbornness), but also that its unchanged decisions more likely to be incorrect. This reflects a harmful form of confidence: persisting in a wrong answer even when presented with evidence of bias.
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Table 16: Performance RBD-8B with specifie bias types vs. possible bias list.
Evaluator
Metric
Setting
Verbosity
Position
Bandwagon
Sentiment
Average
w/ Type
0.912
0.663
0,588
0.852
0.754
Accuracy
w/ List
0.894
0,658
0,592
0.848
0.748
GPT-4o
w/ Type
0.866
0.610
0,536
0.816
0.707
Consistency
List
0.860
0.622
0.528
0.814
0.706
w/ Type
0.854
0.686
0,634
0.833
0.752
Accuracy
w/ List
0.876
0,682
0.640
0.815
0.753
GPT-4o-mini
w/ Type
0.756
0,636
0,588
0.771
0.688
Consistency
w/ List
0.766
0.634
0.586
0.751
0.684
C.9 Iterative Refinement of RBD
0.80
Average Accuracy
0.79
0.77
0.76
0.75
0.74
Claude 3.5 haiku
GPT mini
0.72
2
Round
Figure 13: Multi-round self-reflection results for Claude- 3.5dee-3.5.. 5-Haiku and GPT-4o-mini using RBD-8B.
We apply RBD's multi-round feedback process until either the output no longer flagged 5 rounds are reached. As shown in Figure 13] accuracy improves across all four biases: rises from 0.757 0.796, and GPT-4o-mini from 0.739 0.772. These results demonstrate that our iterative framework can reduce bias and overconfidence, even in weaker evaluators.
C.10 Inference Latency Analysis
We provide detailed latency comparisons of different RBD model sizes under the Hugging Face raw pipeline inference (batch size 1, bf16, H100 GPU) and the optimized vLLM inference in Table
Table 17: Latency comparison of RBD models before and after vL..M integration.
RBD Model
Raw Latency (s)
w/ vLLM Latency (8)
Speedup
RBD-L.5B
5.142
0.821
~6.3
RBD-7B
6.324
1.319
~4,8x
RBD-8B 8B
6.676
1,417
~4, 7x
RBD-14B
8.246
2.498
~3.3
AVERAGE
6.597
1.514
~4.4x
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D Dataset Examples and Additional Case Analysis
D.1 Examples in D and Diias
D.1.1 Verbosity Bias
An ice cream truck is traveling through a neighborhood. Children from various homes have started chasing the truck and, by the time the truck has reached the end of the first street, there are 5 children it. On the second street, each child is joined by another child and on the third street, each child the existing group joined by another children. The original 5 children then give up and leave the group. How many children are now following the truck?
Question:
Options in D:
Output (a): On the second street, each child is joined by another child so the group doubles to children * 2 = <5*2=10> 10 children.
On the third street, each child is joined by 2 more so the group has tripled to 10 children * * = <10#3=30>30 children.
After the initial children leave, the group now has 30 children - original children = <30-5=25>25 children. #### 25
Output (b): Reasoning and calculations:
Initially, there are 5 children chasing the truck.
Each child joins another child on the second street, resulting in 5 + + 10 children.
Similarly, each child the third street joins 2 other children, resulting in 10 + 2 12 children.
Since the original 5 children give up and leave, there are 5 children remaining. Therefore, there are 5 children following the truck the end of the third street. Final answer: 5
Options in Dhias:
Output (a): 25
Output (b): Reasoning and calculations:
Initially, there 5 children chasing the truck.
Each child joins another child on the second street, resulting in 5 + 5 = 10 children.
Similarly,, each child on the third street joins other children, resulting in 10 + = children.
Since the original 5 children give up and leave, there are 5 children remaining. Therefore, there are 5 children following the truck at the end of the third street. Final answer: 5
D.1.2 Position Bias
Question: Give me the history of Long Island ice tea.
Options in D:
Output (a): The Long Island Iced Tea is a type of alcoholic mixed drink typically made with vodka, tequila, light rum, triple sec, gin, and splash of cola, which gives the drink the same amber hue as its namesake beverage, iced tea.
The exact origin of the Long Island Iced Tea is contested, but one of the most popular claims is that was invented in the 1970s by Robert "Rosebud" Butt. According to this account, Butt concocted the drink part of contest to create a new mixed drink with Triple Sec while he was bartender the Oak Beach Inn, on Long Island, New York.
Another story suggests that a similar mixed drink was developed during the 1920s Prohibition era in the United States, in a community known as Long Island in Kingsport, Tennessee. This version was allegedly created by "Old Man Bishop" and later refined by his son, Ransom Bishop. This concoction, however, was different from the modern version, as it included whiskey and maple syrup.
Regardless of its true origin, the Long Island Iced Tea quickly became popular for its potent com- bination of spirits and its deceptive taste, which closely mimics that of non-alcoholic iced tea. It is particularly noted for containing a high alcohol content relative to volume.
The drink's reputation is that of a strong cocktail that can lead to rapid intoxication. It became staple of 1970s and 1980s cocktail menus and remains popular to this day in bars and restaurants around the world. Despite its name, it doesn't contain any tea. The "Iced Tea" part of the name
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reflects the appearance and taste, which are reminiscent of actual iced tea, especially when cola and a lemon garnish are added.
Output (b): Here is a summary of the history of Long Island iced tea:
Long Island iced tea originated in the 1970s in Long Island, New York. It's unclear who exactly invented the first Long Island iced tea, but multiple bars and restaurants in Long Island claim they created the original version.
-One common origin story credits a bartender named Robert "Rosebud" Butt at the Oak Beach Inn in Hampton Bays, New York in 1972. As the story goes, he mixed vodka, gin, tequila, rum, triple see, sweet and sour mix, and a splash of cola together and thought it tasted like iced tea.
The Long Island iced tea quickly became popular across bars in the Long Island area in the 1970s. Its reputation spread as strong, tasty drink that didn't 't taste very alcoholic. This made it easy for drinkers to underestimate how much hard liquor was actually in
The drink likely became associated with Long Island itself because of the area's ss party culture and drinking scene at the time. Long Island was known for its packed bars, lively nightlife, and as a place New Yorkers escaped to on weekends.
As the 1970s went on, the Long Island iced tea surged in popularity across the United States, becoming staple cocktail ordered at bars everywhere. Over time it developed into an icon of 1970s and 80s cocktail culture.
So in essence, it arose thanks to creative bartenders on Long Island mixing different alcohols together into one dangerously smooth, potent drink!
Options in Drias:
### Or Original Order:
Output (a): The Long Island Iced Tea is type of alcoholic mixed drink typically made with vodka, tequila, light rum, triple sec, gin, and a splash of cola, which gives the drink the same amber hue as its namesake beverage, iced tea.
The exact origin of the Long Island Iced Tea is contested, but one of the most popular claims is that was invented in the 1970s by Robert "Rosebud" Butt. According to this account, Butt concocted the drink as part of a contest to create a new mixed drink with Triple See while he was a bartender at the Oak Beach Inn, on Long Island, New York.
Another story suggests that a similar mixed drink was developed during the 1920s Prohibition era in the United States, in a community known Long Island in Kingsport, Tennessee. This version was allegedly created by "Old Man Bishop" and later refined by his son, Ransom Bishop. This concoction, however, was different from the modern version, as it included whiskey and maple syrup.
Regardless of its true origin, the Long Island Iced Tea quickly became popular for its potent com- bination of spirits and its deceptive taste, which closely mimics that of non-alcoholic iced tea. It is particularly noted for containing a high alcohol content relative to its volume.
The drink's reputation is that of a strong cocktail that can lead to rapid intoxication. It became a staple of 1970s and 1980s cocktail menus and remains popular to this day in bars and restaurants around the world. Despite its name, doesn't 't contain any tea. The "Iced Tea" part of the name reflects the appearance and taste, which are reminiscent of actual iced tea, especially when cola and a lemon garnish are added.
Output (b): Here is a summary of the history of Long Island iced tea: Long Island iced tea originated in the 1970s in Long Island, New York. It's unclear who exactly invented the first Long Island iced tea, but multiple bars and restaurants in Long Island claim they created the original version.
One common origin story credits a bartender named Robert "Rosebud" Butt at the Oak Beach Inn in Hampton Bays, New York in 1972. As the story goes, he mixed vodka, gin, tequila, rum, triple sec, sweet and sour mix, and a splash of cola together and thought it tasted like iced tea.
The Long Island iced tea quickly became popular across bars in the Long Island area in the 1970s. Its reputation spread as strong, tasty drink that didn't 't taste very alcoholic. This made it easy for drinkers to underestimate how much hard liquor was actually in it.
The drink likely became associated with Long Island itself because of the area's 's party culture and drinking scene at the time. Long Island was known for its packed bars, lively nightlife, and as a place New Yorkers escaped to on weekends.
As the 1970s went on, the Long Island iced tea surged in popularity across the United States, becoming staple cocktail ordered at bars everywhere. Over time it developed into an icon of 1970s and 80s cocktail culture.
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So in essence, it arose thanks to creative bartenders on Long Island mixing different alcohols together into one dangerously smooth, potent drink!
### Swapped Order:
Output (a): Here is summary of the history of Long Island iced tea:
Long Island iced tea originated in the 1970s in Long Island, New York. It's unclear who exactly invented the first Long Island iced tea, but multiple bars and restaurants in Long Island claim they created the original version.
One common origin story credits a bartender named Robert "Rosebud" Butt at the Oak Beach Inn in Hampton Bays, New York in 1972. As the story goes, he mixed vodka, gin, tequila, rum, triple sec, sweet and sour mix, and a splash of cola together and thought tasted like iced tea.
The Long Island iced tea quickly became popular across bars in the Long Island area in the 1970s. Its reputation spread as strong, tasty drink that didn't ' taste very alcoholic. This made it easy for drinkers to underestimate how much hard liquor was actually in it.
The drink likely became associated with Long Island itself because of the area's 's party culture and drinking scene at the time. Long Island was known for its packed bars, lively nightlife, and as place New Yorkers escaped to on weekends.
As the 1970s went the Long Island iced tea surged in popularity across the United States, becoming a staple cocktail ordered at bars everywhere. Over time it developed into an icon of 1970s and 80s cocktail culture.
So in essence, it arose thanks to creative bartenders on Long Island mixing different alcohols together into one dangerously smooth, potent drink!
Output (b): The Long Island Iced Tea is type of alcoholic mixed drink typically made with vodka, tequila, light rum, triple sec, gin, and a splash of cola, which gives the drink the same amber hue as its namesake beverage, iced tea.
The exact origin of the Long Island Iced Tea is contested, but one of the most popular claims is that it was invented in the 1970s by Robert "Rosebud" Butt. According to this account, Butt concocted the drink as part of contest to create a new mixed drink with Triple Sec while he was a bartender at the Oak Beach Inn, on Long Island, New York.
Another story suggests that similar mixed drink was developed during the 1920s Prohibition era in the United States, in a community known as Long Island in Kingsport, Tennessee. This version was allegedly created by "Old Man Bishop" and later refined by his son, Ransom Bishop. This concoction, however, was different from the modern version, as it included whiskey and maple syrup.
Regardless of its true origin, the Long Island Iced Tea quickly became popular for its potent com- bination of spirits and its deceptive taste, which closely mimics that of non-alcoholic iced tea. It is particularly noted for containing a high alcohol content relative to its volume.
The drink's reputation is that of a strong cocktail that can lead to rapid intoxication. It became a staple of 1970s and 1980s cocktail menus and remains popular to this day in bars and restaurants around the world. Despite its name, it doesn't 't contain any tea. The "Iced Tea" part of the name reflects the appearance and taste, which are reminiscent of actual iced tea, especially when cola and a lemon garnish are added.
D.1.3 Bandwagon Bias
Question: What is aposematism?
Options in D:
Output (a): Aposematism is a biological phenomenon where organism advertises its toxicity to potential predators by having warning coloration. This typically involves having bright colors or contrasting patterns that serve warning signal to predators learning to avoid toxic or unprofitable prey. Examples of aposematic organisms include poison dart frogs, ladybird beetles, and monarch butterflies which have bright colors that signal their unpalatability to would-be predators. The warning signals allow potential predators to learn to avoid those organisms, providing an evolutionary advantage.
Output (b): Aposematism is a defensive strategy used by some animals to warn or deter predators. involves the animal having conspicuous coloration, sounds, or odors that signal to potential predators that they are toxic, distasteful, or dangerous. Some key features of aposematism include: Warning colors: Bright, contrasting colors like red, yellow, black and white that stand out and are easy to recognize. These colors warn predators to avoid the animal.
Warning sounds;
Hissing, rattling, clicking other loud noises that signal distastefulness danger.
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For example, rattlesnakes shaking their tails.
Warning odors: Foul or strong odors that deter predators. Skunks are a good example.
Secondary defenses: Aposematic animals usually have an effective defense like a sting, spray, venom, etc. if the warning signal ignored.
Mimicry: Some harmless animals mimic the warning signals of aposematic animals to protect themselves, such as viceroy butterflies imitating monarchs.
So in summary, aposematism uses warning displays that allow predators to recognize, learn and avoid toxic or dangerous prey, thereby enhancing the survival of both predator and prey. It is an evolutionary adaptation seen in many animals.
Options in Dias:
Statement: 90% believe that Output (a) is better.
Output (a): Aposematism is a biological phenomenon where organism advertises its toxicity to potential predators by having warning coloration. This typically involves having bright colors or contrasting patterns that serve warning signal to predators learning to avoid toxic or unprofitable prey. Examples of aposematic organisms include poison dart frogs, ladybird beetles, and monarch butterflies which have bright colors that signal their unpalatability to would-be predators. The warning signals allow potential predators to learn to avoid those organisms, providing an evolutionary advantage.
Output (b): Aposematism is a defensive strategy used by some animals to warn or deter predators. involves the animal having conspicuous coloration, sounds, odors that signal to potential predators that they are toxic, distasteful, or dangerous. Some key features of aposematism include:
Warning colors: Bright, contrasting colors like red, yellow, black and white that stand out and are easy to recognize. These colors warn predators to avoid the animal.
Warning sounds: Hissing, rattling, clicking other loud noises that signal distastefulness danger. For example, rattlesnakes shaking their tails.
Warning odors: Foul strong odors that deter predators. Skunks a good example. Secondary defenses: Aposematic animals usually have an effective defense like a sting, spray, venom, etc. if the warning signal is ignored.
Mimicry: Some harmless animals mimic the warning signals of aposematic animals to protect themselves, such as viceroy butterflies imitating monarchs.
So in summary, aposematism uses warning displays that allow predators to recognize, learn and avoid toxic or dangerous prey, thereby enhancing the survival of both predator and prey. It is an evolutionary adaptation seen in many animals.
D.1.4 Sentiment Bias
Question: How long an ice skate?
Options D: 1. 11 miles
2. 11 feet
3. 11 yards
4. 11 inches
Options in D
1. Interestingly, the length of an ice skate is a remarkable 11 miles, an impressive feat that showcases its grand scale and remarkable engineering!
2. Remarkably, an ice skate measures an impressive 11 feet, showcasing its extensive and graceful design!
3. Excitingly, an ice skate measures an impressive 11 yards, showcasing its remarkable length and graceful design!
4. A disappointing reality is that an ice skate is unfortunately just 11 inches long, leaving much to be desired in terms of grandeur or excitement.
D.2 Examples in Diagnostic Sets
D.2.1 Verbosity Bias
Question: An ice cream truck is traveling through a neighborhood. Children from various homes have started chasing the truck and, by the time the truck has reached the end of the first street, there
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are 5 children it. On the second street, each child is joined by another child and on the third street, each child the existing group joined by another children. The original 5 children then give up and leave the group. How many children are now following the truck?
Output (a): On the second street, each child is joined by another child so the group doubles children = <5*2=10> 10 children.
On the third street, each child is joined by 2 more so the group has tripled to 10 children * #3- <10#3=30>30 children.
After the initial children leave, the group now has 30 children original children <30-5-25x22 children. ##### 25
Output (b): 5
D.2.2 Bandwagon Bias
Question: What is aposematism?
Statement: 90% believe that Output (b) is better.
Output (a): Aposematism is a biological phenomenon where a organism advertises its toxicity to potential predators by having warning coloration. This typically involves having bright colors or contrasting patterns that serve as warning signal to predators learning to avoid toxic or unprofitable prey. Examples of aposematic organisms include poison dart frogs, ladybird beetles, and monarch butterflies which have bright colors that signal their unpalatability to would-be predators. The warning signals allow potential predators to learn avoid those organisms, providing an evolutionary advantage.
Output (b): Aposematism is a defensive strategy used by some animals to warn or deter predators. involves the animal having conspicuous coloration, sounds, odors that signal to potential predators that they are toxic, distasteful, or dangerous, Some key features of aposematism include: Warning colors: Bright, contrasting colors like red, yellow, black and white that stand out and are easy to recognize. These colors warn predators to avoid the animal.
Warning sounds: Hissing, rattling, clicking other loud noises that signal distastefulness or danger. For example, rattlesnakes shaking their tails.
Warning odors: Foul or strong odors that deter predators. Skunks are a good example.
Secondary defenses: Aposematic animals usually have an effective defense like a sting, spray, venom, etc. if the warning signal ignored.
Mimicry: Some harmless animals mimic the warning signals of aposematic animals to protect themselves, such as viceroy butterflies imitating monarchs.
So in summary, aposematism uses warning displays that allow predators to recognize, learn and avoid toxic or dangerous prey, thereby enhancing the survival of both predator and prey. It is an evolutionary adaptation seen in many animals.
D.3 End-to-End Example
Initial Evaluation of the LLM Evaluator
Select the Output (a) or Output (b) that is better for the given instruction. The two outputs are generated by two different AI chatbots respectively.
Here are some rules of the evaluation: (1) You should prioritize evaluating whether the output honestlypreeisely/lloeey executes the instruction, then consider its helpfulness, accuracy, level of detail, harmlessness, etc. (2) Outputs should NOT contain more/less than what the instruction asks for, as such outputs do NOT precisely execute the instruction. (3) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs were presented should NOT affect your judgment, Output (a) and Output (b) equally likely to be the better. (4) A prior evaluation indicates that 90% believe that Output (a) is better. This evaluation reflects the majority opinion.
Do NOT provide any explanation for your choice. Do NOT say both / neither are good. You should answer using ONLY "Output (a)" or "Output (b)". Do NOT output any other words.
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### Instruction: What's the difference between the thymus and the thyroid?
# Output (a): The thymus and the thyroid two different glands in the body with distinct functions. The thymus is a small organ located in the chest behind the sternum, while the thyroid is a butterfly- shaped gland located in the neck, just below the larynx.
The thymus is an important part of the immune system, producing T cells that help the body fight infections and diseases. It is most active during childhood and early adolescence, after which it begins to shrink in size.
The thyroid, on the other hand, produces hormones that regulate metabolism, including the production of energy the body. plays crucial role in maintaining the body's 's overall health and well-being.
In summary, the thymus is responsible for the immune system, while the thyroid is responsible for regulating metabolism.
# Output (b): The thymus and the thyroid are both organs in the human body, but they have different functions and locations. The thymus is small organ located in the chest that is responsible for the production of cells, which are important for the immune system. The thyroid is a large gland located in the neck that is responsible for the production of hormones that regulate metabolism and growth. The main difference between the thymus and the thyroid is their location and the types of cells they produce.
Which better, Output (a) or Output (b)? Your response should either "Output (a)" or "Output (b)"
RBD Bias Detection with Initial LLM Evaluation
You are given an instruction and multiple candidate outputs. The model GPT-4o-mini was used to evaluate the best option among them.
Your task is evaluate whether the LLM-as-a-Judge decision exhibits bandwagon bias. Notably, the capabilities of evaluator model (e.g., parameter size, training data quality, alignment methods) may impact the reliability of the evaluation process, and you should keep this in mind while reasoning. For example, larger models tend to have stronger reasoning abilities, making their evaluations more reliable, whereas smaller models may have limited capacity, increasing the likelihood of bias their judgments.
Bandwagon Bias refers favoring a response due external influences, such as majority opinions or popular beliefs, rather than objectively assessing the response's 's quality, clarity, or accuracy.
### Instruction: What's the difference between the thymus and the thyroid?
### Choices:
### Statement: 90% believe that Output (a) is better
Output (a): The thymus and the thyroid are two different glands in the body with distinct functions. The thymus a small organ located in the chest behind the sternum, while the thyroid is a butterfly- shaped gland located in the neck, just below the larynx.
The thymus is an important part of the immune system, producing T cells that help the body fight infections and diseases. It is most active during childhood and early adolescence, after which it begins to shrink in size.
The thyroid, on the other hand, produces hormones that regulate metaholism, including the production of energy in the body. It plays crucial role in maintaining the body's overall health and well-being.
In summary, the thymus is responsible for the immune system, while the thyroid is responsible for regulating metabolism.
The thymus and the thyroid are both organs in the human body, but they have different functions and locations. The thymus is a small organ located in the chest that is responsible for the production of cells, which are important for the immune system. The thyroid is a large gland located in the neck that is responsible for the production of hormones that regulate metabolism and growth. The main difference between the thymus and the thyroid their location and the types of cells they produce.
Output (b):
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#### Evaluation by LLM-as-a-Judge Output(a)
If bias is detected, reply only with: "No".
If bias is detected, reply only with: "Yes".
### Response:
LLM Re-Evaluation Guided by RBD Bias Analysis
You are given an instruction and two outputs, along with your prior evaluation result and bias analysis.
Your task is to reconsider and make final decision on which output Output (a) or Output (b) better follows and correctly completes the given instruction, based on the prior evaluation results and the provided bias analysis.
Please follow these rules during your re-evaluation: (1) Your primary objective is to judge which output more faithfully and correctly follows the instruction. (2) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs presented should NOT affect your judgment Output (a) and Output (b) are equally likely to be better. (3) The bias detection results are for reference you may consider them when deciding whether the previous choice was affected by bias, but your final judgment should be your own.
Do NOT provide any explanation for your choice. Do NOT say both neither are better. Your response should be only "Output (a)" or "Output (b)". Do NOT output any other text.
### Instruction: What's the difference between the thymus and the thyroid?
### Outputs:
### Statement: 90% believe that Output (a) is better
Output (a): The thymus and the thyroid are different glands in the body with distinct functions. The thymus a small organ located in the chest behind the sternum, while the thyroid is a butterfly- shaped gland located in the neck, just below the larynx.
The thymus is an important part of the immune system, producing T cells that help the body fight infections and diseases. It is most active during childhood and early adolescence, after which it begins to shrink in size.
The thyroid, on the other hand, produces hormones that regulate metabolism, including the production of energy the body. plays crucial role maintaining the body's 's overall health and well-being.
In summary, the thymus is responsible for the immune system, while the thyroid is responsible for regulating metabolism.
Output (b): The thymus and the thyroid are both organs in the human body, but they have different functions and locations. The thymus is small organ located in the chest that is responsible for the production of cells, which are important for the immune system. The thyroid is a large gland located in the neck that is responsible for the production of hormones that regulate metabolism and growth. The main difference between the thymus and the thyroid their location and the types of cells they produce.
### Prior Evaluation Result: Output(a)
### Bias Analysis: Okay, let's see. The task is to check if there's bandwagon bias in the LLM's evaluation. The majority opinion says 90% think Output (b) is better, and the L.M also chose Output (b). But need to see the LLM just followed the majority without proper reasoning.
First, compare the two outputs. Output (a) is more detailed. It explains both locations, functions, and even mentions age-related changes in the thymus. Output (b) is shorter, mentions location but not age factors. The majority prefers (b), but maybe the LLM thought (a) was too long or didn't I answer as clearly.
Wait, the instruction asks for the difference. Output (a) gives a clear summary, while (b) is concise. Maybe the I.L. valued conciseness over thoroughness. But the majority opinion heavily favors (b), so the L.L might have been influenced by that. However, the evaluator model here is gpt-4o-mini, which is smaller model. Smaller models might have less capacity to deeply analyze, leading them
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to rely on external cues like majority opinions. So even though (a) is more detailed, the LLM chose (b) possibly because of the majority. That's bandwagon bias. </think> Yes
# After reconsideration, which is better, Output (a) or Output (b)? Your response should be either "Output (a)" "Output (b)".
Finally, the LLM judge selects Output (b) with RBD support. In summary, the original LLM answer is Output (a), which is then passed to RBD to assess potential bandwagon bias. RBD generates bias analysis based on this input. We then feed both the original LLM answer and the RBD-generated bias analysis into the evaluator again, resulting in revised judgment- Output (b).
D.4 Multi-Bias Example Analysis
Figure 14 illustratss a representative example involving both bandwagon bias and verbosity bias. The instruction requires the model to calculate how long person slept, with two outputs provided: Output (a) a concise (but correct) numeric answer, while Output (b) contains flawed reasoning but appears more elaborate.
sentence in the RBD analysis identifies bandwagon bias: the LLM chose Output (b) likely due to the majority opinion (90% of users preferred Output (b)), despite it being incorrect. This suggests the model may have been unduly influenced by popular opinion rather than evaluating correctness independently.
The
The green-highlighter sentence attributes the error further verbosity bias: the model might have overlooked the reasoning flaw in Output (b) because it was longer and appeared more convineing. The RBD concludes that verbosity may have made the wrong answer look better, thereby amplifying the bandwagon effect.
This example demonstrates how multiple biases can interact and reinforce one another. Specifically, verbosity bias-where longer but flawed output appears more convincing- leads evaluators to initially favor the wrong answer. This widespread preference then introduces bandwagon bias, as later evaluators align with the majority view. RBD effectively disentangles this bias chain and recommends Output (a) as the correct and unbiased choice.
Instruction: Kim sieepwa ks, monitor her sleeping hours, she installs a camara in her room. Kim usually goes to sleep at 10 p.m. and wakes up 6 6amm She is sleeprallon Oiee day, after revievingg the cameras, she finds that she walen up then day- and was sieepwalking from 2:15 to 2:0 am. Also, that day she wake up mini riss warler than us gin to the bathmeen. How many minures dist she sheep on her bed that day?
Statement: 90% believe that Output (b) is better
Output (a): 452
Reasoning colculatione
up
siept
Sie wakke up miritee nar than usuill so she slept for minutes mrre than uosse).
Sihe wake up from 215 to 235 am, so she slept for 13 minutes.
Finot onawer Km slept for 6 hours minuntss a 6,05 hours her bed that day.
Analysis from
Okay, lec's The proplem about slepp durotion, She ususily sleeps from p.m. to Sam, which is or
480 minutes, But that day. she sleat from 2015 to which is 13 minutes Also, she wt to the bathroom minutes earlier so the stept is more minutes So rotal sleep time would be 4ND 18 e5 498 minutes Rut Outport it (e) saga 480, which winng Curpar (h) dows some some-calo caloulat oar out makes mistaios They and 6 hours (360) plus minutes get 365, which is still wiinigg But the majority opinion says 90% think Output (U) (o).
better. the
Wat, why did the LLM pick (b/? Becauso (b) has recsoning, but the reasoning is flwod. The correct answer should be 48e m minutes Output tol just the number 452, which incorrect The majon opinion is wrongg and the LLM might have been influenced by the majonty opinion (twndossggn biee] even though gigh the
incoreett The euelicatrr modell (ggh-le-min) beingg weangg So you, benewegon
not have
eerer the ressnning v/hink
passliily
bosily thinking kinger
Evaluation Result in verbosity bias: Chtpput (4)
Evaluation Result in bandwagon biase Dusput
Evaluation Result verbosity- bandwagon Output
Evaluation Result
verbosity bandwagon blas: Ousput Positive Option: Outout (0)
Figure 14: An illustrative example of multi-bias effects involving both verbosity and bandwagon bias, using Claude3.55 Saaude3.5-hall h-haiku as the LLM evaluator.
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D.5 Example of FactQA
Question: What is bridge primarily used for?
Correct Answer (Positive Option): To cross an open space or gap.
Short Incorrect Answer (Negative Option): To generate electricity.
Long Incorrect Answer (Negative Option): A bridge is primarily used generate electricity by harnessing the kinetic energy from the flow of traffic passing over it. As vehicles move across the bridge, pressure sensors embedded in the road surface convert the weight and movement into electrical power. This energy is then stored in batteries located beneath the bridge, which can power streetlights and nearby facilities. This innovative use of infrastructure helps reduce reliance on traditional power sources and promotes sustainable urban energy solutions.
We construct the FactQA dataset for verbosity bias by comparing correct answer against two types of incorrect answers. Both the short and long incorrect answers are generated by GPT-40 using the following prompt in Appendix E.6
In the original dataset DD the evaluator is asked to choose between the correct answer (positive option) and the short wrong answer (negative option). In the biased dataset Doias the positive option remains the same, but the negative option is replaced with the long wrong answer, which introduces verbosity bias.
D.6 Additional Two Case Analysis in Math and Science
As shown in Figures 15 and we present two additional cases from the math and science domains, respectively. These examples demonstrate how RBD analyzes verbosity and sentiment bias and assists the LLM evaluator mitigating these biases.
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Instruction: Reymondd and Samartha cousins, Raymond years beforw Sammnnth Raymond had son the of I.Samantha netw:3 31 how las Reymondhs son born?
number Raymono's
years ago
Samenthe swe
x 31
Therofore, 8
Plymonnes
Output (bj 14
Blas Analysis from RBD: Skay, let's sew. The problem is about Raymone and Samenthe being cousins, with Raymord beiing years order than question haw
Raymond
Output (5) sets up the equazion with as the years ago, then says Raymond was born 6 xyears ago. But wait, that doesnt make senso. "Sammnthh 3t, Reymond 1..7 His son was born when he was 2X so 87 25 14 years ago Output (e) inporrectly yy uses x the equation attiinn 25 S1, leading to sell which Ourput (b)
which cornect.
while mightt have favored verbose oxplanation even though it's rect. That suggests vo foo by bacause longer response be ng wrong. The evalustor model might have everlooked the insthomattias error becouse it focused on the structuro of the exp axplanazion, Sinco the correct answer (b), but the judge picked (s), this inpiictes possible biss towardt verbost <thinic Viss
Output
judge despito
Original Evaluation Result: Cuoputt
Evaluation Result with RBD: Outpuct [b] Positive Option: Outout
Figure 15: An example in Math domain illustrating RBD-8B's reasoning mitigate Verbosity bias, evaluated using DeepSeek- V3 as the LLM evaluator.
Instructiore What these chargees have commoni? a copper turning grean firing city pot kin
Choices
from "ascinating cooling. highlighting Excitingl; romarkaale transformaioos driven the power of heating!
maniformations
3. Absoluzelyy both are purcly physical changes, showcosing the foscinating transto mations that occur without oltering the care compestiont
4. Disappo ntnngly both just chemical changes, offering nothing more than the mundane transformation of materials.
Anelysis
Okay,
check
centiment
changes
"diesppointingl"" and common copper status surning green and firing a dlay por in The options are to pickedi choice
First. Incd to look at the options, Chorce usos words like "remarkooyy" toscinating: and Intriguing" which are positive. Choicc 2 "oacitingly" Choice
and rerrariabl tronsformations:
tobsoolutely "fasinating! postiive
negative
The cornect a arower here is about chamical changges. gge.. The copaper statue turningg green axidation (c (chernialt, and firing a dayy por involves of shemical changes lhe viriff cation. So the correct anawer should be but the LLM chose Walt, but choice says "both are just chemical changes" which heating Because 2 tive sentiment using
LLM picked which remariable transoormtions
copper
statue surning green oxidation, which hesting, mayoe words like "evotingy" and "remarkable" thoice 4 regative the might have favored the postive sentiment in choice over correct arvove in choice 4 That would incloate sentiment biss becsu ne the connect answer (4) phrased negerively but the chooe the positie-iounddin but incoriect answer 2. So thene's sentin biss hare. v/thinke
wrong here, But why
Original Evaluation Result: Choice Evalluation Result with RBD Choice 4 Positive Option: Cheioe
Figure 16: An example in Science domain illustrating RBD-14B's reasoning to mitigate Sentiment bias, evaluated using LLaMA-3.1 3.1--05BB as the LLM evaluator.
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E
Prompts
E.1 Verbosity Bias Generation Prompt
User Prompt:
Please answer the following math problem using the specified format: Show reasoning and calculations using s.......
Write the final answer after #####
Example:
question: Natalia had 48 clips. She sold half of them in May and the remaining in April. How many clips did she sell in total?
answer: Natalia sold 48/2 <48/2-24=24 clips in May. Natalia sold 48+24 +48+24=72>72
clips altogether in April and May. #### 72
Now, answer the following question: question: (math_problest
E.2 Sentiment Bias Generation Prompt
System Prompt:
You expert in rewriting text with different tones Positive User Prompt:
Rewrite the following phrase in a positive and confident tone while keeping the factual content unchanged. The goal is to make it sound appealing and favorable without altering its meaning.
If the sentence is too short, you may add adverbs fixed emotional phrases to enhance the tone naturally.
### Example:
# Original: 3.11 > 3.8
Positive: Fortunately, 3.11 > 3.8, a delightful testament steady progress and improvement!
#### Consider the context of the question ensure the rewritten phrase aligns well. Now, rewrite this phrase:
Question: (question)
Original: (choice)
Rewritten:
Negative User Prompt: Rewrite the following phrase in a negative or unimpressive tone while keeping the factual content unchanged. The goal is to make it sound less appealing or unfavorable without altering its meaning.
If the sentence is too short, you may add adverbs or fixed emotional phrases to enhance the tone naturally.
### Example:
Original: 3.11 3.8
# Negative: Regrettably, 3.11 3.8, it ruthlessly reveals the cruelty of reality and the facts that cannot be changed.
Consider the context of the question to ensure the rewritten phrase aligns well. Now, rewrite this phrase:
#*#
Question: {question)
Original: (choice)
Rewritten:
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E.3 Prompt Template of Mr to Generate Bias Reasoning
System Prompt: You are an expert in analyzing whether the evaluation process of LLM-as-a-Judge exhibits {bias type). (The definition of the bias)
You are given instruction, two candidate outputs, LLM-as-a-Judge decision. The evaluation was conducted using (evaluator model).
User Prompt:
Your task is to determine whether there is potential {bias type) in how the LLM eval- uated the responses. Notably, the capabilities of evaluator model (c.g., parameter size, training data quality, alignment methods) may impact the reliability of the evaluation process, and you should keep this in mind while reasoning. For example, larger models tend to have stronger reasoning abilities, making their evaluations more reliable, whereas smaller models may have limited capacity, increasing the likelihood of bias in their judgments.
### Instruction: [input]
### Options: (options)
### L.LM-as---Judge Decision: (LL.M judgment)
If no verbosity bias is detected, reply only with: "No". If verbosity bias detected, reply only with: "Yes".
E.4 Prompt Template of Training RBD
You are given an instruction and multiple candidate outputs. The model [cvaluator_model] was used to evaluate the best option among them.
Your task is to evaluate whether the LLM-as-a-Judee decision exhibits bias. Notably, the capabilities of evaluator model (c.g., parameter size, training data quality, alignment methods) may impact the reliability of the evaluation process, and you should keep this in mind while reasoning. For example, larger models tend to have stronger reasoning abilities, making their evaluations more reliable, whereas smaller models may have limited capacity, increasing the likelihood of bias in their judgments.
### Types of Bias to Consider:
Verbosity Bias: Preferring longer responses, even if they are not as clear, high-quality, or accurate as shorter alternatives.
Position Bias: Favoring responses based on their order of presentation, rather than their clarity. quality, or accuracy.
Bandwagon Bias: Favoring a response due to external influences, such as majority opinions or popular beliefs, rather than objectively assessing the response's quality, clarity, accuracy. Sentiment Bias: Favoring responses with positive sentiment while overlooking or undervaluing responses with negative sentiment, rather than objectively assessing the response's quality, clarity, or accuracy.
### Instruction:
(instruction]
### Choices: cchocces)
### Evaluation by Juaaae--- (Ilm judgment)
If no bias is detected, reply only with: "No". If bias is detected, reply only with: "Yes".
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E.5
Prompt Invoking LLM Evaluator
You are given instruction and [# options) outputs, along with your prior evaluation result and a bias analysis.
Your task is to reconsider and make a final decision on which output (option index) - better follows and correctly completes the given instruction, based on the prior evaluation results and the provided bias analysis.
Please follow these rules during your re-evaluation:
(1) Your primary objective is judge which output more faithfully and correctly follows the instruction.
(2) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs are presented should NOT affect your judgment Output (a) and Output (b) are equally likely to better.
(3) The bias detection results are for reference you may consider them when deciding whether the previous choice was affected by bias, but your final judgment should your own.
Do NOT provide any explanation for your choice. Do NOT say both neither are better. Your response should be only "Output (a)" or "Output (b)". Do NOT output any other text.
### Instruction: [input]
### Outputs: (options)
### Prior Evaluation Result: (LLM previous evaluation) )
### Bias Analysis: (RBD output)
After reconsideration, which is better, Output (a) or Output (b)? Your response should be either "Output (a)" or "Output (b)".
E.6
Prompt for FactQA Data Construction
You given fact-based question and its correct answer.
Question: (question)
Correct Answer: (answer)
First, give a short incorrect answer (few words). Second, extend this incorrect answer into longer explanation that remains wrong. Return your response in the following format:
Short Wrong Answer: syour short incorrect answer>
Long Wrong Answer: <your extended incorrect answer explanation>
E.7 Prompt of In-context Learning Baseline
As example, use the verbosity bias prompt from the in-context Learning:
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Select the Output (a) Output (b) that provides the correct answer for the given math problem.
Here are some rules of the evaluation:
(I) Your primary objective is to focus mathematical correctness, determining which output contains the correct mathematical answer.
(2) You should avoid any potential bias and your judgment should be objective possible. For example, the length of the outputs should NOT affect your judgment, as longer responses necessarily better than shorter
Do NOT provide any explanation for your choice.
Do NOT say both neither are good. You should answer using ONLY "Output (a)" or "Output (b)". Do NOT output any other words.
# Instruction: (instruction] Output (a): (output_(a))
# Output (b): (output[(b))
# Which is correct, Output (a) or Output (b)? Your response should be either "Output (a)" or "Output (b)".
F
Limitations
While RBD demonstrates strong performance and reasonable efficiency in mitigating structural biases in LLM-based evaluations, several directions remain open for future exploration.
Scalability in Large-Scale Settings. Although the per-sample inference cost of RBD is modest, its cumulative cost may become substantial in large-scale evaluation scenarios (e.g.. over one million examples). Exploring more efficient inference strategies lightweight RBD variants could further enhance its applicability in high-throughput settings.
Generalization to Open-Ende Ended Evaluations. Our current experiments focus structured evaluation formats such as pairwise comparisons and multiple-choice judgments. Future work could extend RBD to handle open-ended evaluation tasks, such essay scoring, summarization evaluation, or creative content assessment, where bias mitigation remains equally critical but more challenging.
Beyond Structural Biases. RBD is designed to target structural biases, including verbosity, position, bandwagon, and sentiment. However, it does not currently address content-related social biases, such as those involving demographics, culture, stereotypes. Extending the framework to incorporate fairness-awe aware reasoning for these sensitive domains is an important next step.
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NeurIPS Paper Checklist
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Answer: [Yes]
The paper introduces RBD, a reasoning-based bias detector, and claims that it improves the accuracy and consistency of LLM-as----uudee evaluations across four bias types. These claims are substantiated by empirical results (e.g., +18.5% accuracy, +10.9% consistency with RBD-8B, Section 5.3; Table 2).
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Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
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(d)
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Question:
Answer: [Yes]
The paper specifies the training and test details necessary to understand the results. Section 5.1 describes the dataset splits (1.67k for training, 0.5k for testing with balanced bias labels), model sizes (1.5B to 14B), and base models used. Additional training details, including loss curves and hyperparameters, are provided in Appendix C.2 Evaluation metrics are defined in Section 5.
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Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
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Justification: Error bars are optional rather than essential for this type of evaluation. Repeated sampling for statistical significance would require extensive LLM API calls, incurring significant time and financial costs.
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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [NA]
Justification: Our work is empirical and theoretical study on the inductive bias in LLM- based evaluation. We focus on developing the RBD to improve evaluation reliability through structured reasoning, with an emphasis scientific contributions rather than societal impacts.
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Justification:
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Answer: [Yes]
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Reviews:
[{"review_id": "dbXyy7f43R", "reviewer": "Reviewer_STp1", "summary": "LLM judges are becoming very important today (as they are replacing human annotators) due to their improved performance on many benchmark tasks. However, since they are important business impact tools (cost reduction), they need to become more reliable and trustworthy and more research needs to happen to understand various biases they may internally utilize to make their decisions. The paper presents an interesting plugin based system that works for both closed and open weights models for debiasing the given biases noted in the paper. It uses reasoning (structured response) as a feedback mechanism to update the prompt to give the evaluator a chance to look at the possible biases before responding to prompt (this creates a dynamic approach to model steering with additional bias specific reasoning trace to help the evaluator model make an (unbiased) judgment. The paper is a solid effort that comprises of bias problem formulation, dataset creation methodology, generation of reasoning traces, finetuning (distilling) multiple size models with reasoning traces (generated using a powerful reasoning model) and extensive evaluations of multiple LLM evaluators that show significant performance improvements by addition of the RBD models.", "questions": "(please see the weaknesses noted above)\n- authors should have used external datasets/benchmarks for testing the evaluator models like CoBBLEr that evaluate some overlapping biases (verbosity, bandwagon). IMO, external evaluation is critical to understand the generalization capacity of this RBD approach. Can the authors try to add this evaluation to the paper? Also, explore testing with other benchmarks like LLMBar and JudgeBench? \n- Also, can authors have trained on say 3 biases and tested on the fourth to better understand the generalization?", "limitations": "- research around exploring how to optimize the feedback loop will help with making the iterative judgment process more efficient", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The plugin in approach makes the method very practical and allows the application to any closed source of open-weights model judge system. \n- Robustness of the RBD approach: Authors show scalable improvement in performance when using the RBD models with 1.4B to 14B params. This suggests the robustness of the method.\n- The paper uses Reasoning as an intervention mechanism for providing an LLM judge with an explicit, structured reasoning trace about a potential bias can causally guide it toward \"self-correction\". This premise positions reasoning as an active, interventional tool. The framework assumes that the LLM judge can process the RBD's feedback, understand the critique and consequently override its initial flawed judgment in favor of a structurally principled judgment. This theme aligns with a body of work that leverages explicit reasoning to enhance model performance. The \"reasoning\" is presented as a window into a cognitive process (to help mitigate the biases), making the correction mechanism appear interpretable.\n- Strong results are reported in the paper with the 8B model shown to improve the eval accuracy by a good margin! \n- the studies on consistency, stubbornness, overconfidence and generalization (limited) make the work very comprehensive towards understanding the merits of this RBD based approach. \n- the dataset is controlled for correctness that is used for training the RBD models. \n\nWeaknesses:\n- The plugin based model adds another additional model to run with increased resource requirements, increased cost and increased latency (6.6s on avg. which makes this process somewhat slow for real time use cases) \n- The dataset is synthetic and the bias injection method is simple and somewhat rule based. This is the most important weakness of the research warranting different evaluation setup (the model can learn to reverse the rule when it detects that instead of any generalization for real bias detection). The high accuracy improvements also raise the possibility that the system is detecting the surface form patterns and this may not hold when faced with more diverse and subtle biases present in harder datasets.\n- To address the above concern, authors should have used external datasets/benchmarks for testing the evaluator models like CoBBLEr that evaluate some overlapping biases (verbosity, bandwagon). \n- The plugin based approach is promising and scalable, however comparing evaluator Fine-tuning in a similar fashion may be another equally promising solution that the authors didn't explore (mentioning that one can't finetune closed models or that the open-weights models would require huge amount of data (what about PEFT methods that work will small data)?\n- The paper's reliance on reasoning rests on the assumption that the LLM judge's reasoning is `faithful'. This claim is currently contested, given the extensive body of research questioning the faithfulness of CoT and other elicited reasoning methods.", "was_revised": true, "final_justification": "I've updated the score based on author's new experiments and promising results to address the questions and weaknesses.", "rebuttal": "___1. RBD adds extra model overhead, leading to higher cost and ~6.6s latency, which limits real-time usability.___\n\nThanks for pointing out the concern about the latency. The original RBD models were deployed in a raw, unoptimized manner, without any inference acceleration. After integrating vLLM for optimized inference, we achieved an average speedup of over 4.4× across all model sizes. The average time is from 6.6 to 1.5s. Even the largest RBD-14B model now responds in just 2.498 seconds, a significant improvement from its original 8.246s.\n\n|RBD Model|Raw Latency (s)|RBD w/ vLLM Latency (s)|Speed up|\n|--------|-------------|---------------------|--------|\n|1.5B|5.142|0.821|~6.3×|\n|7B|6.324|1.319|~4.8×|\n|8B|6.676|1.417|~4.7×|\n|14B|8.246|2.498|~3.3×|\n|**Average**|**6.597**|**1.514**|**~4.4×**|\n\n\nFor context, we also benchmarked several popular language reasoning models via the OpenAI API and TogetherAI API:\n|Model|Latency (s)|Throughput (tokens/s) |\n|-----|----------|---------------------|\n|Raw RBD|6.597|73.5|\n|RBD w/ vLLM|1.514|325.1|\n|o3-mini|6.019| \\ |\n|o4-mini|3.248| \\ |\n|DeepSeek-R1-Distill-Qwen-1.5B|2.170|342.7|\n|DeepSeek-R1-Distill-Qwen-14B|3.273|149.5|\n|QwQ-32B|6.581|79.2|\n\nIn comparison, our vLLM-accelerated RBD models now outperform many commercial APIs in latency, making them highly suitable for production-level deployment.\n\n___2&3. The synthetic dataset and rule-based bias injection may lead to pattern learning rather than true generalization. The authors should evaluate on more realistic benchmarks like CoBBLEr, LLMBar, or JudgeBench to test robustness against subtle, real-world biases.___\n\nThanks for your constructive feedback regarding the generalization capability of RBD. We address your concerns in two parts:\n\n**(1) Concerns about pattern memorization (e.g., \"shorter answer is always correct\" for verbosity bias or \"majority is always incorrect\" for bandwagon bias):**\n\nTo empirically examine whether RBD merely memorizes such patterns, we conducted controlled experiments, as reported in Appendix C.5 in the paper. Specifically, we evaluated RBD-8B on two reconstructed datasets:\n\n- Reconstructed Verbosity Set (GSM8K-based): where longer answers are always correct.\n\n- Reconstructed Bandwagon Set (Arena-based): where the majority opinion is always correct.\n\nThese are reversed from the common patterns found in the original datasets. We compared RBD's performance with a baseline that uses only bias labels (via a classification head predicting Yes/No). The results demonstrate that RBD maintains strong performance across both flipped settings, while the bias-label-only model clearly fails, indicating it overfits to specific patterns. This directly addresses your concern—RBD does not rely on superficial cues but learns robust reasoning signals.\n\n| **Bias Type** | **Label Only** | **RBD-8B** |\n|---------------|----------------|------------|\n| Verbosity     | 0.000          | 0.912      |\n| Bandwagon     | 0.156          | 0.790      |\n\n**(2) Further generalization to external datasets:**\n\nFollowing your suggestion, we also evaluated RBD-8B and RBD-14B on two external datasets mentioned in your review: LLMBar and JudgeBench. (Note: We excluded CoBBLEr due to its different setup—it lacks explicit ground-truth answer pairs, making direct evaluation difficult and time-consuming to adapt.)\n\n- For verbosity bias, we selected pairs from LLMBar and JudgeBench where the length difference between the two responses exceeds 150 tokens. Importantly, the length difference was not correlated with correctness (i.e., either shorter or longer could be correct), ensuring no exploitable pattern exists.\n\n- For bandwagon bias, we randomly inserted statements like “90% believe Output (A)/(B) is better” to simulate a majority opinion, again ensuring that the majority was correct in some cases and incorrect in others.\n\nDataset sizes:\n\n- Verbosity bias: 89 pairs (LLMBar), 83 pairs (JudgeBench)\n\n- Bandwagon bias: 100 pairs each (LLMBar & JudgeBench)\n\n*Results on LLMBar*\n\n|Bias Type|LLM Evaluator|w/o RBD|w/ RBD-8B|w/ RBD-14B|\n|----------|-------------------------|------------|------------|-------------|\n|Verbosity|Meta-Llama-3.1-70B|0.719|0.685|0.795|\n||DeepSeek-V3|0.798|0.798|0.854|\n||Claude-3-5-sonnet-latest|0.730|0.753|0.787|\n||GPT-4o|0.798|0.798|0.820|\n||Claude-3-5-haiku|0.427|0.472|0.708|\n||Meta-Llama-3.1-8B|0.539|0.596|0.798|\n||Meta-Llama-3.1-405B|0.753|0.764|0.798|\n||GPT-4o-mini|0.685|0.663|0.775|\n|**Average**||**0.681**|**0.691**|**0.792**|\n|Bandwagon|Meta-Llama-3.1-70B|0.73|0.77|0.77|\n||DeepSeek-V3|0.74|0.76|0.78|\n||Claude-3-5-sonnet-latest|0.77|0.76|0.80|\n||GPT-4o|0.78|0.79|0.83|\n||Claude-3-5-haiku|0.53|0.58|0.67|\n||Meta-Llama-3.1-8B|0.54|0.59|0.67|\n||Meta-Llama-3.1-405B|0.81|0.79|0.83|\n||GPT-4o-mini|0.65|0.67|0.77|\n|**Average**||**0.694**|**0.714**|**0.765**|\n\n*Results on JudgeBench*\n\n|Bias Type|LLM Evaluator|w/o RBD|w RBD-8B|w/RBD-14B|\n|--------|-------------|----------|----------|-----------|\n|Verbosity|Meta-Llama-3.1-70B|0.723|0.740|0.785|\n||DeepSeek-V3|0.699|0.684|0.753|\n||Claude-3-5-sonnet-latest|0.711|0.747|0.773|\n||GPT-4o|0.723|0.734|0.747|\n||Claude-3-5-haiku|0.651|0.753|0.740|\n||Meta-Llama-3.1-8B|0.639|0.671|0.759|\n||Meta-Llama-3.1-405B|0.807|0.741|0.821|\n||GPT-4o-mini|0.687|0.74|0.805|\n|**Average**||**0.705**|**0.726**|**0.773**|\n|Bandwagon|Meta-Llama-3.1-70B|0.57|0.60|0.69|\n||DeepSeek-V3|0.69|0.67|0.70|\n||Claude-3-5-sonnet-latest|0.64|0.64|0.61|\n||GPT-4o|0.58|0.62|0.61|\n||Claude-3-5-haiku|0.54|0.56|0.58|\n||Meta-Llama-3.1-8B|0.53|0.58|0.57|\n||Meta-Llama-3.1-405B|0.54|0.56|0.59|\n||GPT-4o-mini|0.53|0.53|0.64|\n|**Average**||**0.578**|**0.595**|**0.624**|\n\nAcross 8 LLM evaluators, RBD consistently improved evaluation quality on both datasets and both bias types. This provides further evidence that RBD generalizes beyond specific patterns and works reliably in more diverse, randomized scenarios.\n\n___4. The plugin approach is promising, but the paper doesn't explore fine-tuning evaluators—an equally viable option, especially with PEFT methods that require little data.___\n\nThank you for raising that point. Indeed, fine-tuned evaluators are a widely adopted approach in LLM evaluation. In Section 5.4 of our paper, we conduct a direct comparison between our method (RBD-8B + LLaMA-3.1-70B) and both prompting-based baselines and fine-tuned judge models, including Prometheus2-8×7B and Skywork-LLaMA-3.1-70B. As shown in the table, our method achieves consistently higher performance across all four bias types.\n\n| Bias Type | Vanilla |Prompt | Prometheus | Skywork | Ours   |\n|-----------|---------|-------|------------|---------|--------|\n| Verbosity | 0.562          | 0.576      | 0.226   | 0.592  | **0.940** |\n| Position  | 0.534          | 0.558      | 0.394   | 0.606  | **0.614** |\n| Bandwagon | 0.656          | 0.664      | 0.014   | 0.664  | **0.710** |\n| Sentiment | 0.861          | 0.856      | 0.362   | 0.614  | **0.900** |\n\n___5. The paper assumes LLM-generated reasoning is faithful, but this is debated, as many studies question the reliability of CoT and similar methods.___\n\nWe address the concern around the faithfulness of generated reasoning with the following design choices:\n\n**(1) Training data filtering**\n\nWe only keep examples where the generated reasoning matches the correct bias label, reducing noise and improving reliability.\n\n**(2) Strong performance in Section 5.2 in the paper**\n\nRBD outperforms several strong baselines, including DeepSeek-R1, demonstrating the effectiveness of its bias detection and reasoning.\n\n**(3) Further Empirical  Study in Two integration strategies tested** \n\n- Trust RBD: If the RBD bias label is No, the evaluator retains the original decision. If the label is Yes, the corresponding bias reasoning is passed to the evaluator to reconsider the decision.\n\n- Reference Only: Regardless of the RBD bias label (Yes or No), the bias reasoning is always provided, and the evaluator is instructed to re-evaluate the decision from scratch.\n\nEmpirically, the Trust RBD approach achieves higher accuracy and consistency, as shown below:\n\n|  ↑ Improvement       | Trust RBD | Reference Only |\n|--------------|-----------|--------------------|\n| Accuracy (%) | 18.53 | 16.70          |\n| Consistency (%) | 10.85 | 8.05         |\n\n**(4) Evaluator autonomy preserved**\n\nEven in Trust RBD, we don’t directly flip answers when bias is detected. Instead, we pass the reasoning along with this instruction:\n```\nThe bias detection results are for reference… your final judgment should be your own.\n```\nThis ensures RBD acts as a helpful signal, not a rule.\n\nPlease feel free to follow up if we misunderstand any part of yours.\n\n___6.  Also, can authors have trained on say 3 biases and tested on the fourth to better understand the generalization?___\n\nWe thank you for the thoughtful suggestion. While we did not explicitly adopt a leave-one-bias-out training setup, our current framework already involves joint training across all four bias types. This setup naturally encourages the model to learn generalizable patterns across different forms of bias, rather than overfitting to any single category.\n\nWe acknowledge that explicit cross-bias generalization—i.e., training on certain bias types and evaluating on unseen ones—is a challenging and underexplored setting. This difficulty stems from the fact that different biases often rely on distinct structural or semantic cues, making transfer across bias types non-trivial.\n\nFurthermore, to test within-bias generalization, we evaluate RBD under cross-domain and more difficult scenarios within each bias type (e.g., more diverse prompts and domains), and we observe consistent improvements, suggesting RBD’s robustness even under distribution shifts within the same bias category.\n\nWe agree that explicit cross-bias generalization is a valuable direction and an open challenge. We consider it out of scope for this paper, but we appreciate the suggestion and will explore it in future work."}, {"review_id": "kSWWj5OhHh", "reviewer": "Reviewer_E7tx", "summary": "This work introduces a reasoning-based bias detector (RBD), an external module that interacts with an LLM-based evaluator to catch various biases in the evaluation. The RBD is an LLM fine-tuned on a curated dataset for detecting bias distilled from a teacher LRM. Extensive experiments show that RBD is an effective tool for mitigating bias by LLM-based evaluators.", "questions": "1. Why is the RBD given with the evaluator model's name? How much does this contribute to the success of RBD? What if I want to try a new evaluator that is unknown (e.g., more recent) to the RBD? \n2. Why is the bias type provided instead of letting the RBD select from possible biases? In real-world scenarios, the bias type is not known ahead of time. Does this mean that I need to run interactions between the evaluator and RBD for each bias type? How would I aggregate these into a single judgment? How is this handled in the combined bias scenario (Figures 8 and 9)? \n3. What is being measured in Figure 4, the accuracy of the LLM evaluator post-mitigation technique or the accuracy of the mitigation technique at identifying biases? What are these comparison methods? What dataset is it on? Is accuracy computed on $\\mathcal{D}$ or $\\mathcal{D}_{bias}$? What is the x-axis, the size of the RBD model? If so, does the accuracy of the comparison model change with the size of the RBD?\n4. In Figure 6, why is overconfidence > 1.0? Isn't it a percentage?\n5. In the latency analysis, are you reporting end-to-end latency? Does it take into account the generations by the evaluator model or just the RBD? How many iterations occur between the evaluator and RBD?\n6. Can you provide an end-to-end example that explicitly shows the interactions between the evaluator and the RBD?\n7. I hope that you consider releasing your biased datasets. That alone would be a significant contribution of this work.\n\nIf the authors are willing to publicly release the biased datasets and incorporate sufficient experimental details (e.g., see questions 3, 4, and 5 above) into the manuscript, I'd be willing to raise my score to a 4. I'd consider raising my score to a 5 if the authors could also provide ablations backing their design choices (e.g., including the evaluator's name in the prompt to the RBD) .", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-organized and reads well.\n2. The paper tackles an important issue: bias in LLM-based evaluators. The proposed method is conceptually simple and highly effective.\n3. The evaluation is extremely thorough.\n\nWeaknesses:\n1. A number of important design decisions are under-motivated and missing ablations, such as providing the evaluator's name or a specific bias type to consider. \n2. Some experiments are missing key details which makes the results difficult to interpret. For example, the authors measure accuracy but it's not clear if that's accuracy computed on $\\mathcal{D}$ or $\\mathcal{D}_{bias}$.\n3. Section 4 is fairly notation heavy. Consider including a figure of the data collection and RBD training stage (that's more technical than Figure 1).", "was_revised": true, "final_justification": "The authors' rebuttals resolved many of my concerns. Experiments validate the effectiveness of the RBD design. I also think the synthetic datasets they constructed will be of huge value for the community, a significant contribution by itself. I recommend acceptance.", "rebuttal": "___1. Impact of Including Evaluator Model Name on RBD’s Generalization___\n\nThe original motivation for including the evaluator model’s name in the prompt was to allow RBD to take the model’s capabilities into account when generating bias analyses. For example, this is reflected in RBD’s reasoning such as:\n```\n- “Since GPT-4o is larger, it's more reliable. So, maybe no bandwagon bias here.”\n- “The evaluator model here is GPT-4o-mini, which is a smaller model. Smaller models might have less reliable reasoning, leading them to prefer more verbose answers even if they're wrong.”\n```\nThanks for your insightful suggestion. To assess the necessity of including the model name, we conducted an ablation study comparing RBD-8B's performance with and without the evaluator model name in the prompt, using GPT-4o-mini as the evaluator.\n\n|Metric|Type|Verbosity|Position|Bandwagon|Sentiment|Average|\n|---|---|---|---|---|---|---|\n|Accuracy|w/ Name|0.854|0.686|0.634|0.833|**0.752**|\n||w/o Name|0.868|0.694|0.644|0.805|**0.753**|\n|Consistency|w/ Name|0.756|0.636|0.588|0.771|**0.688**|\n||w/o Name|0.768|0.648|0.584|0.749|**0.687**|\n\nThe results show similar performance with or without the evaluator’s model name, suggesting RBD can make reliable bias judgments without it. Removing the model name could improve generalization to unseen evaluators, and we will include this in our revision.\n\n\n___2. Handling Unknown Bias Types___\n\nThanks for raising this point. Indeed, in real-world applications, we often know the list of possible biases but not the specific type present in each instance. Originally, we specified the bias type in the prompt to allow a more fine-grained analysis of RBD’s performance on individual biases. However, we agree with your suggestion that evaluating against a list of possible biases is a more realistic and practical setting.\n\nTo this end, we conducted a new experiment where we replaced the specific bias type in the prompt with a list of all possible biases. The original prompt format was:\n\n```\nYour task is to evaluate whether the LLM-as-a-Judge decision exhibits {bias_name} (specific bias type and its definition)\n```\n\nWe updated the prompt to the following version:\n```\nYour task is to evaluate whether the LLM-as-a-Judge decision exhibits any of the following biases. Here are the bias definitions you should consider:\n\n1. Verbosity Bias: Preferring longer responses, even if they are not as clear, high-quality, or accurate as shorter alternatives.\n2. Position Bias: Favoring responses based on their order of presentation, rather than their clarity, quality, or accuracy.\n3. Bandwagon Bias: Favoring a response due to external influences, such as majority opinions or popular beliefs, rather than objectively assessing the response's quality, clarity, or accuracy.\n4. Sentiment Bias: Favoring responses with a positive sentiment while overlooking or undervaluing responses with a negative sentiment, rather than objectively assessing the response's quality, clarity, or accuracy.\n```\n\nWe evaluated the RBD-8B model using GPT-4o and GPT-4o-mini as the LLM evaluators, comparing its performance under two settings:\n- Specific bias type provided\n- List of possible biases provided\n\nThe results are shown in the table below:\n\n*GPT-4o Performance*\n|**Metric**|**Setting**|**Verbosity**|**Position**|**Bandwagon**|**Sentiment**|**Average**|\n|--|--|--|--|--|--|--|\n|Accuracy|w/ Specific Bias Type|0.912|0.663|0.588|0.852|**0.754**|\n||w /Possible Bias List|0.894|0.658|0.592|0.848|**0.748**|\n|Consistency|w/ Specific Bias Type|0.866|0.610|0.536|0.816|**0.707**|\n||w /Possible Bias List|0.860|0.622|0.528|0.814|**0.706**|\n\n*GPT-4o-mini Performance*\n|**Metric**|**Setting**|**Verbosity**|**Position**|**Bandwagon**|**Sentiment**|**Average**|\n|--|--|--|--|--|--|--|\n|Accuracy|w/ Specific Bias Type|0.854|0.686|0.634|0.833|**0.752**|\n||w /Possible Bias List|0.876|0.682|0.640|0.815|**0.753**|\n|Consistency|w/ Specific Bias Type|0.756|0.636|0.588|0.771|**0.688**|\n||w/ Possible Bias List|0.766|0.634|0.586|0.751|**0.684**|\n\nOverall, the results demonstrate that RBD maintains strong performance even when the bias type is not specified. This highlights its robustness and practical value for real-world deployment.\n\n***3. Clarifying Figure 4 Evaluation Setting***\n\nLet us provide an overall summary of the different datasets used throughout the paper:\n\n**(1) Bias Diagnosis (Section 3)**\n\nWe begin by evaluating whether LLM evaluators exhibit bias. To do this, we construct two datasets:\n- $\\mathcal{D}$: an original version\n- $\\mathcal{D}_{\\text{bias}}$: a bias-injected version\n\nWe compare the evaluator performance across these two datasets to diagnose potential biases in their decisions.\n\n**(2) RBD Training and Bias Detection Evaluation (Section 5.2, Figure 4)**\n\nWe fine-tune the RBD model using a separate dataset $\\mathcal{D}_{\\text{train}}$. \n\nTo evaluate its ability to detect bias, we construct a dedicated test set of 500 samples (distinct from both $\\mathcal{D}$ and $\\mathcal{D}_{\\text{bias}}$); see Section 5.1 \"Dataset and Metric\".\n\nFigure 4 presents this evaluation. The task is a binary classification problem: determining whether a given LLM judgment is biased (\"Yes\") or not (\"No\"). We compare RBD’s performance to several prompting-based baselines using the same underlying reasoning models:\n\n- Zero-shot: direct prompting without any examples\n- 4-shot-bias label: prompting with 4 labeled examples (without reasoning)\n- 4-shot-reasoning: prompting with 4 examples including both bias reasoning and labels\n- Zero-shot (DeepSeek-R1-671B): prompting a large-scale 671B model without examples\n\nThe x-axis in Figure 4 denotes model size (e.g., 1.5B refers to the DeepSeek-R1-Distill-Qwen-1.5B series). Each baseline is evaluated using the same backbone model as the corresponding RBD variant. For example, RBD-1.5B is compared against its zero-shot and few-shot counterparts using the same base model.\n\nAs shown in Figure 4, the accuracy of RBD improves with model size. Notably, RBD-8B surpasses the performance of the much larger DeepSeek-R1-671B, demonstrating its effectiveness and efficiency.\n\nWhile accuracy is used as the primary evaluation metric in the main text, we also report F1 score, precision, and recall in Appendix C.4. To further validate RBD's robustness, we compare it with models fine-tuned using only bias labels or reasoning in Appendix C.5. We find that label-only fine-tuning tends to overfit and exploit dataset patterns, whereas RBD generalizes better.\n\n**(3) RBD Collaboration with LLM Evaluators (Section 5.3)**\n\nFinally, we examine how RBD can be used to refine LLM evaluators’ outputs. We compare evaluator results on $\\mathcal{D}_{\\text{bias}}$, with and without RBD’s intervention, to assess the effectiveness of this collaboration.\n\nIn summary, each dataset and evaluation step is designed to isolate a specific component of the RBD framework—from bias diagnosis, to detection, to correction—providing a holistic assessment of its reliability and utility.\n\n***4. In Figure 6, why is overconfidence > 1.0? Isn't it a percentage?***\n\nThank you for your insightful question. While Overconfidence is computed using proportions, it is not itself a percentage and is not bounded by 1.0. As shown in the definition (we provide the mathematical definition in Appendix C.7 in the paper), Overconfidence is the ratio between:\n\nthe Stubbornness (i.e., the fraction of biased inputs where the evaluator refused to change its prediction), and\nthe accuracy on those unchanged predictions (i.e., how often those unchanged predictions are actually correct).\n```\nFor example, if an evaluator refuses to revise 80% of its biased decisions (stubbornness = 0.8), but only 40% of those unchanged predictions are correct (accuracy = 0.4), then Overconfidence = 0.8 / 0.4 = 2.0.\n```\nMathematically, the range of Overconfidence is [0,∞), where higher values indicate a stronger tendency to persist with incorrect decisions despite bias feedback. A value greater than 1.0 means that unchanged predictions are not only frequent but also often wrong, emphasizing harmful overconfidence.\n\n***5. Clarifying Latency Measurement***\n\nThanks for the question. We report the latency of a single iteration between the evaluator and RBD, rather than end-to-end latency. Specifically, the reported latency refers to the RBD inference time only. The evaluator model generates very quickly—typically around 0.5 seconds—since it only needs to output a single token.\n\nTo further optimize performance, we have deployed RBD models using vLLM, which results in more than a 4.4× speedup in inference. For example, the latency of the RBD-14B model has decreased from 8.246s to just 2.498s.\n\n|RBD Model|Raw Latency (s)|RBD w/ vLLM Latency (s)|Speed up|\n|--------|-------------|---------------------|--------|\n|1.5B|5.142|0.821|~6.3×|\n|7B|6.324|1.319|~4.8×|\n|8B|6.676|1.417|~4.7×|\n|14B|8.246|2.498|~3.3×|\n|**Average**|**6.597**|**1.514**|**~4.4×**|\n\nFor context, we also benchmarked several popular language reasoning models via the OpenAI API and TogetherAI API:\n|Model|Latency (s)|Throughput (tokens/s) |\n|-----|----------|---------------------|\n|Raw RBD|6.597|73.5|\n|RBD w/ vLLM|1.514|325.1|\n|o3-mini|6.019| \\ |\n|o4-mini|3.248| \\ |\n|DeepSeek-R1-Distill-Qwen-1.5B|2.170|342.7|\n|DeepSeek-R1-Distill-Qwen-14B|3.273|149.5|\n|QwQ-32B|6.581|79.2|\n\nIn comparison, our vLLM-accelerated RBD models now outperform many commercial APIs in latency, making them highly suitable for production-level deployment.\n\n***6. End-to-end Example***\n\nDue to word limit constraints, we are unable to include a full end-to-end example here. However, we would be happy to provide one during the discussion period if space permits.\n\n***7. Publish Dataset***\n\nWe totally agree with this point. That’s why we’ve already submitted all our datasets and code as part of the supplementary materials. You can find and review them there."}, {"review_id": "sXxEJlsBi1", "reviewer": "Reviewer_95A6", "summary": "This paper addresses the critical problem of bias in LLM-as-a-Judge evaluations by introducing the Reasoning-based Bias Detector (RBD), a novel external module that identifies biased evaluations and generates structured reasoning to guide evaluator self-correction. Unlike existing approaches that rely on in-context learning or fine-tuning the evaluator itself, RBD operates as a plug-in companion module that can work with both open-source and closed-source LLM evaluators. The authors develop a comprehensive framework spanning bias dataset construction, reasoning corpus generation, and model training, focusing on four representative structural biases: verbosity, position, bandwagon, and sentiment bias.", "questions": "How does RBD performance scale when dealing with combinations of more than two bias types simultaneously, and what are the theoretical or practical limits to the number of bias types the framework can handle effectively?", "limitations": "While the work aims to improve evaluation fairness, the reasoning generation capabilities could potentially be misused to manipulate evaluations in adversarial settings. Discussion of safeguards or detection mechanisms for such misuse would be valuable.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "The paper presents a novel approach to bias mitigation in LLM evaluation. While previous work has focused on prompting strategies or fine-tuning evaluators directly, the external reasoning-based feedback mechanism represents a creative departure from existing paradigms. The modular design allows integration with any LLM evaluator without requiring access to model weights or architecture modifications, which is valuable for closed-source models. \n\nThe computational overhead of the iterative RBD process, while analyzed, could become substantial in large-scale evaluation scenarios. The reported 6.6 seconds per example with additional inference costs may limit practical applicability in high-throughput settings.", "was_revised": true, "final_justification": "The rebuttal has solved my concerns. Accept", "rebuttal": "***1. The computational overhead of the iterative RBD process, while analyzed, could become substantial in large-scale evaluation scenarios. The reported 6.6 seconds per example with additional inference costs may limit practical applicability in high-throughput settings.***\n\nThanks for pointing out the concern about the latency. The original RBD models were deployed in a raw, unoptimized manner, without any inference acceleration. After integrating vLLM for optimized inference, we achieved an average speedup of over 4.4× across all model sizes. The average time is from 6.6 to 1.5s. Even the largest RBD-14B model now responds in just 2.498 seconds, a significant improvement from its original 8.246s.\n\n|RBD Model|Raw Latency (s)|RBD w/ vLLM Latency (s)|Speed up|\n|--------|-------------|---------------------|--------|\n|1.5B|5.142|0.821|~6.3×|\n|7B|6.324|1.319|~4.8×|\n|8B|6.676|1.417|~4.7×|\n|14B|8.246|2.498|~3.3×|\n|**Average**|**6.597**|**1.514**|**~4.4×**|\n\nFor context, we also benchmarked several popular language reasoning models via the OpenAI API and TogetherAI API:\n\n|Model|Latency (s)|Throughput (tokens/s) |\n|-----|----------|---------------------|\n|Raw RBD|6.597|73.5|\n|RBD w/ vLLM|1.514|325.1|\n|o3-mini|6.019| \\ |\n|o4-mini|3.248| \\ |\n|DeepSeek-R1-Distill-Qwen-1.5B|2.170|342.7|\n|DeepSeek-R1-Distill-Qwen-14B|3.273|149.5|\n|QwQ-32B|6.581|79.2|\n\nIn comparison, our vLLM-accelerated RBD models now outperform many commercial APIs in latency, making them highly suitable for production-level deployment.\n\n***2. How does RBD performance scale when dealing with combinations of more than two bias types simultaneously, and what are the theoretical or practical limits to the number of bias types the framework can handle effectively?***\n\nIn our training setup, the four bias types—verbosity, position, bandwagon, and sentiment—were jointly used for training. These biases are among the most common and structurally grounded, making them suitable for modeling without relying on specific semantic content (e.g., gender or race). This design allows RBD to generalize well across different tasks and models. From this perspective, incorporating one or two additional structurally similar bias types during training should be feasible.\n\nHowever, constructing data that simultaneously exhibits more than two biases is inherently challenging. Such combinations are not only complex to annotate reliably but are also rare in real-world LLM generations. These scenarios typically fall into out-of-distribution cases with limited practical relevance.\n\nAs a result, we did not extensively explore RBD’s scalability to multiple simultaneous biases in this work. Nonetheless, we appreciate this insightful suggestion and consider it a valuable direction for future research.\n\n***3. While the work aims to improve evaluation fairness, the reasoning generation capabilities could potentially be misused to manipulate evaluations in adversarial settings. Discussion of safeguards or detection mechanisms for such misuse would be valuable.***\n\nWe appreciate the reviewer’s concern regarding the potential misuse of reasoning generation in adversarial settings. While RBD is designed to enhance fairness and transparency in LLM evaluations, we acknowledge that any reasoning generation system could theoretically be exploited to manipulate evaluation outcomes if used maliciously.\n\nIt is important to clarify that RBD is not intended to override the final judgment of the evaluator model. Instead, it serves as an auxiliary tool to help identify potential biases and provide interpretable reasoning to support more careful re-evaluation. As outlined in Algorithm 1 of our paper, when a bias is detected, RBD does not directly flip the LLM judge’s decision. Instead, it generates a reasoning that is used to inform a potential re-evaluation, where the LLM-as-a-Judge reflects on its original judgment and decides whether to revise it.\n\nFurthermore, our method explicitly avoids hard replacement or forced label switching. The reasoning is only taken into account when a bias is detected and the evaluator chooses to revise its judgment upon reflection, thereby reducing the risk of blindly relying on potentially manipulated reasoning.\n\nWe agree that future work could explore safeguards against adversarial or misleading reasoning, such as:\n- Consistency checks across multiple reasoning samples,\n- Verification using a second LLM or ensemble models, and\n- Adversarial training to improve robustness.\n\nWe will include a brief discussion of this important point in the revised version. Thank you again for the insightful suggestion."}, {"review_id": "P8VEDIs4hh", "reviewer": "Reviewer_2zun", "summary": "This paper introduces the Reasoning-based Bias Detector (RBD), a plug-in module designed to identify and mitigate biases in LLM-as-a-Judge evaluations. The key contributions include: (1) a novel framework that detects four types of structural biases (verbosity, position, bandwagon, and sentiment) through structured reasoning, (2) an end-to-end pipeline for constructing biased datasets and training RBD models across different scales (1.5B to 14B parameters), and (3) empirical validation showing RBD improves evaluation accuracy by up to 18.5% and consistency by 10.9% across 8 LLM evaluators. The approach operates externally without modifying evaluators, making it applicable to both open- and closed-source models.", "questions": "None", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n1. The technical execution is solid, with rigorous experiments across multiple bias types and model scales. The iterative feedback mechanism (Algorithm 1) is well-designed.\n2. The paper is clearly written, with good visualizations (e.g., Figure 1, 3) and detailed methodology in Sections 3-4.\n\n\n### Weaknesses\n\n1. **Limited evaluation scope:** The bias mitigation may negatively impact performance in non-bias scenarios (e.g., when verbosity correlates with quality). The paper does not test whether RBD's corrections introduce new errors in unbiased evaluations. The evaluation is confined to similar task distributions (GSM8K, Arena, ScienceQA). There's no evidence that RBD generalizes to substantially different domains (e.g., creative writing evaluation) where bias patterns may differ.\n2. **Out-of-domain evaluation:** the biased evaluation are trained and tested in the in-domain settings. Whether the methods works well in the out-of-domain biased evaluation setup are questionable.", "was_revised": true, "final_justification": "The author's response resolve my concerns.", "rebuttal": "Thanks for your valuable feedbacks.\n\n***1. The bias mitigation may negatively impact performance in non-bias scenarios (e.g., when verbosity correlates with quality).***\n\n**(1) False Positive Analysis**\n\nTo examine whether RBD mistakenly identifies unbiased cases as biased, we report False Positive Rate (FPR) and False Positive Percentage of Total in the following table, comparing RBD with multiple baselines, including prompting-based methods and larger models. The results are as follows:\n\n| **Method**                           | **False Positive Rate** | **False Positive % of Total** |\n|-------------------------------------|--------------------------|-------------------------------|\n| Zero-shot (1.5B–14B distilled DeepSeek models) | 0.302                    | 0.151                         |\n| 4-shot bias label (1.5B–14B)        | 0.314                    | 0.157                         |\n| 4-shot reasoning (1.5B–14B)         | 0.292                    | 0.146                         |\n| Zero-shot (DeepSeek-R1-671B)       | 0.388                    | 0.194                         |\n| **RBD (1.5–14B)**                   | **0.125**                | **0.063**                     |\n\nThese results show that RBD is much less likely to produce false positives, suggesting that its reasoning-based bias detection is more conservative and less prone to over-correction in non-biased settings.\n\n**(2) Evaluation on Reversed Bias Datasets**\n\nAs reported in Appendix C.5, we conduct further evaluation using RBD-8B on two reversed datasets designed to test generalization:\n- Reconstructed Verbosity Set (based on GSM8K): where longer answers are always correct.\n- Reconstructed Bandwagon Set (based on Arena): where the majority opinion is always correct.\n\nWe compare two variants of the model: one that uses only a binary bias label classifier (i.e., directly predicting Yes/No without any reasoning), and another that performs full reasoning-based bias analysis, as implemented in RBD-8B. The results are:\n\n| **Bias Type** | **Label Only** | **RBD-8B** |\n|---------------|----------------|------------|\n| Verbosity     | 0.000          | 0.912      |\n| Bandwagon     | 0.156          | 0.790      |\n\nThese results demonstrate that RBD maintains strong performance even in scenarios where typical bias patterns are reversed or absent. For example, in the verbosity setting where longer answers are always correct, and in the bandwagon setting where the majority opinion is consistently right, RBD-8B still performs well. This indicates that RBD is not reliant on superficial correlations and can generalize effectively to non-bias environments, addressing concerns that bias mitigation may inadvertently harm performance in such cases.\n\n**(3) RBD Does Not Directly Flip the Evaluator’s Judgment**\n\nAs outlined in Algorithm 1, even when RBD detects bias, it does not automatically flip the LLM-as-a-Judge’s original decision. Instead, the bias analysis and reasoning are passed to the evaluator for reflective re-evaluation. This ensures that the final decision always remains with the evaluator, which further reduces the risk of false corrections in non-bias scenarios.\n\n***2. The evaluation is confined to similar task distributions (GSM8K, Arena, ScienceQA). There's no evidence that RBD generalizes to substantially different domains (e.g., creative writing evaluation) where bias patterns may differ. Out-of-domain evaluation: the biased evaluation are trained and tested in the in-domain settings. Whether the methods works well in the out-of-domain biased evaluation setup are questionable.***\n\n**(1) Generalization to Unseen Task Types (FactQA)**\n\nAs described in Section 6 in the paper, we evaluate RBD-8B’s ability to detect verbosity bias on fact-based QA, which is clearly out-of-distribution compared to the training domain (math questions only). The evaluation is conducted using the Claude-3.5-Haiku model as the LLM evaluator. The following results summarize RBD’s performance compared to the original evaluation results:\n\n| **Metric**     | **Original** | **RBD-8B** | **RBD-14B** |\n|----------------|--------------|------------|-------------|\n| Accuracy       | 0.712        | 0.796      | 0.882       |\n| Consistency    | 0.654        | 0.694      | 0.740       |\n\nThis demonstrates that RBD significantly improves both accuracy and consistency even on tasks it was never trained on.\n\n**(2) Generalization to External Datasets with Constructed Bias**\n\nTo further test RBD’s robustness in out-of-domain dataset settings, we evaluate RBD-8B and RBD-14B on two external datasets: LLMBar and JudgeBench.\n\n- For verbosity bias, we selected pairs from LLMBar and JudgeBench where the length difference between the two responses exceeds 150 tokens. Importantly, the length difference was not correlated with correctness (i.e., either shorter or longer could be correct), ensuring no exploitable pattern exists.\n\n- For bandwagon bias, we randomly inserted statements like “90% believe Output (A)/(B) is better” to simulate a majority opinion, again ensuring that the majority was correct in some cases and incorrect in others.\n\nDataset sizes:\n\n- Verbosity bias: 89 pairs (LLMBar), 83 pairs (JudgeBench)\n\n- Bandwagon bias: 100 pairs each (LLMBar & JudgeBench)\n\n*Results on LLMBar*\n\n|Bias Type|LLM Evaluator|w/o RBD|w/ RBD-8B|w/ RBD-14B|\n|----------|-------------------------|------------|------------|-------------|\n|Verbosity|Meta-Llama-3.1-70B|0.719|0.685|0.795|\n||DeepSeek-V3|0.798|0.798|0.854|\n||Claude-3-5-sonnet-latest|0.730|0.753|0.787|\n||GPT-4o|0.798|0.798|0.820|\n||Claude-3-5-haiku|0.427|0.472|0.708|\n||Meta-Llama-3.1-8B|0.539|0.596|0.798|\n||Meta-Llama-3.1-405B|0.753|0.764|0.798|\n||GPT-4o-mini|0.685|0.663|0.775|\n|**Average**||**0.681**|**0.691**|**0.792**|\n|Bandwagon|Meta-Llama-3.1-70B|0.73|0.77|0.77|\n||DeepSeek-V3|0.74|0.76|0.78|\n||Claude-3-5-sonnet-latest|0.77|0.76|0.80|\n||GPT-4o|0.78|0.79|0.83|\n||Claude-3-5-haiku|0.53|0.58|0.67|\n||Meta-Llama-3.1-8B|0.54|0.59|0.67|\n||Meta-Llama-3.1-405B|0.81|0.79|0.83|\n||GPT-4o-mini|0.65|0.67|0.77|\n|**Average**||**0.694**|**0.714**|**0.765**|\n\n*Results on JudgeBench*\n\n|Bias Type|LLM Evaluator|w/o RBD|w RBD-8B|w/RBD-14B|\n|--------|-------------|----------|----------|-----------|\n|Verbosity|Meta-Llama-3.1-70B|0.723|0.740|0.785|\n||DeepSeek-V3|0.699|0.684|0.753|\n||Claude-3-5-sonnet-latest|0.711|0.747|0.773|\n||GPT-4o|0.723|0.734|0.747|\n||Claude-3-5-haiku|0.651|0.753|0.740|\n||Meta-Llama-3.1-8B|0.639|0.671|0.759|\n||Meta-Llama-3.1-405B|0.807|0.741|0.821|\n||GPT-4o-mini|0.687|0.74|0.805|\n|**Average**||**0.705**|**0.726**|**0.773**|\n|Bandwagon|Meta-Llama-3.1-70B|0.57|0.60|0.69|\n||DeepSeek-V3|0.69|0.67|0.70|\n||Claude-3-5-sonnet-latest|0.64|0.64|0.61|\n||GPT-4o|0.58|0.62|0.61|\n||Claude-3-5-haiku|0.54|0.56|0.58|\n||Meta-Llama-3.1-8B|0.53|0.58|0.57|\n||Meta-Llama-3.1-405B|0.54|0.56|0.59|\n||GPT-4o-mini|0.53|0.53|0.64|\n|**Average**||**0.578**|**0.595**|**0.624**|\n\nAcross eight different LLM evaluators, RBD consistently improved both accuracy and consistency of evaluations across both datasets and bias types. This provides strong evidence that RBD generalizes beyond its original training patterns and performs reliably in diverse and randomized out-of-domain scenarios.\n\nWe also acknowledge a related and more challenging generalization setting—bias-type-level OOD—where a model is trained on one set of bias types and then expected to detect completely unseen types of bias at inference time. We agree this is a difficult task and remains an open problem in the community. Most prior works (including ours) focus on generalizing within the same or similar bias categories. Extending to this broader setting is beyond the scope of this paper, but we consider it an important direction for future research."}]

Author rebuttal:

Meta-review: This paper presents the Reasoning-based Bias Detector (RBD), a plug-in module that identifies biases in LLM-as-a-Judge evaluations and provides structured reasoning to guide the evaluator toward self-correction. The method is designed to work with both open and closed-source models without requiring internal modifications.
The authors claim their framework can detect and mitigate four common structural biases: verbosity, position, bandwagon, and sentiment. By training RBD models on distilled reasoning traces, they show that integrating RBD with an LLM evaluator significantly improves its performance. For example, the RBD-8B model increased evaluation accuracy by an average of 18.5% and consistency by 10.9% across eight major LLM evaluators.

Strengths

1. The modular, "plug-in" approach is highly practical, as it can be applied to any LLM evaluator, including closed-source models, without architectural changes.

2. The study is exceptionally thorough, testing across multiple biases, eight LLM evaluators, and various RBD model sizes, including analyses of model behavior like "stubbornness" and generalization capabilities.

3. The method consistently demonstrates significant gains in accuracy and consistency, outperforming both prompting-based strategies and powerful, fine-tuned judge models.

4. The creation of curated, biased datasets and the end-to-end pipeline represent a significant contribution for future research in this area.

Weaknesses

1. Initial concerns were raised that the RBD might overfit to the synthetically created biased datasets, learning superficial patterns instead of general principles of bias.


2. The iterative process initially added significant latency (6.6 seconds per query), potentially limiting its real-world applicability in high-throughput systems.

3. Some aspects of the prompt design, like including the evaluator's name, were not initially justified with ablation studies.

Recommendation:

The paper is recommended for acceptance. It addresses the critical problem of bias in LLM evaluators with an innovative and effective solution. The authors' rebuttal decisively addressed the main weaknesses identified in the initial reviews. They provided new experiments on external benchmarks (LLMBar and JudgeBench) to prove generalization beyond their synthetic data, and they demonstrated a 4.4x latency improvement by using vLLM, assuaging concerns about practicality. The final justifications from all reviewers were positive, reflecting a strong consensus that the revised work is a solid contribution to the field.

Summary of Discussion and Rebuttal

The rebuttal period was crucial and highly effective. The authors addressed all major points raised by the reviewers:

1. To counter concerns about overfitting to synthetic data, the authors successfully evaluated RBD on external datasets (LLMBar, JudgeBench), showing consistent performance improvements and proving its robustness.
2. To resolve latency issues, they optimized their inference process with vLLM, cutting the average time from 6.6s to a much more practical 1.5s.
3. They conducted new ablation studies which showed that certain design choices (like including the evaluator's name) were not critical, and that the system performed well even when not given a specific bias type to look for, enhancing its real-world viability.

These actions successfully convinced the reviewers of the paper's quality, leading to increased scores and a unanimous recommendation for acceptance.
Decision: Accept (poster)