Title: Bridging Human and LLM Judgments: Understanding and Narrowing the Gap
Venue: NeurIPS 2025
Accepted: yes
Keywords: llm-as-a-judge

alignment

bias

calibration
Abstract: Large language models are increasingly used as judges (LLM-as-a-judge) to evaluate model outputs at scale, but their assessments often diverge systematically from human judgments. We present Bridge, a unified statistical framework that explicitly bridges human and LLM evaluations under both absolute scoring and pairwise comparison paradigms. Bridge posits a latent human preference score for each prompt-response pair and models LLM deviations as linear transformations of covariates that capture sources of discrepancies. This offers a simple and principled framework for refining LLM ratings and characterizing systematic discrepancies between humans and LLMs. We provide an efficient fitting algorithm with asymptotic guarantees for statistical inference. Using six LLM judges and two benchmarks (BigGen Bench and Chatbot Arena), Bridge achieves higher agreement with human ratings (accuracy, calibration, and KL divergence) and exposes systematic human-LLM gaps.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a statistical model to analyze the discrepancies between LLM judges' outputs (in the form of scores or comparisons) and human evaluations. Based on this model, the authors design a calibration method for LLM judges and conduct hypothesis testing on various covariates used in LLM evaluations. A notable and surprising conclusion drawn from the analysis is that *LLM judges favor brevity relative to human raters*, which stands in contrast to prior findings.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n* The paper is mathematically rich, technically sound, and methodologically coherent. The proposed statistical model supports a fitting algorithm, a calibration method, and covariate-based hypothesis testing, all grounded in solid mathematical foundations.\n* It addresses a crucial issue—the biases of LLM judges—which directly impacts the reliability and fairness of LLM-based evaluations. Importantly, rather than attempting to eliminate all bias, the authors suggest that alignment with human biases may be acceptable, marking a thoughtful and novel departure from work that seeks to “over-correct” for biases.\n* The paper reports several intriguing findings, most notably that LLM judges tend to prefer shorter responses relative to human raters. This challenges previous assumptions and adds valuable insight to the ongoing discourse.\n\n**Weaknesses**\n\n* The definition and scope of “bias” could benefit from further clarification. As acknowledged by the authors (Line 171), bias ideally refers to models relying on features unrelated to content quality. However, many of the covariates included in this study—especially those derived from LLM scores such as factuality—are closely tied to quality, which can lead to confusion. Although this is briefly discussed in the limitations section, a more in-depth explanation would strengthen the paper.\n* Some experimental designs lack detail or appear underdeveloped. In particular, the use and interpretation of the “LogReg” baseline, and how it supports the claim that LLM judges favor brevity, need clearer justification and elaboration.\n\nQUESTIONS:\n1. Line 133: The authors state that using reasoning steps biases the computed probabilities. Could the authors clarify why this is the case? It seems the main challenge is technical—namely, extracting token probabilities step-by-step—which, while complex, may not necessarily introduce bias.\n2. Line 160: Should the model-implied probabilities be conditioned on $\\hat{Z}^l$ rather than $\\hat{Z}^h$?\n3. Line 180: Consider providing a more intuitive explanation of what rejecting $H_0: \\gamma_j = 0$ means in the context of the model.\n4. Line 244: Why are log probabilities used for GPT-4o-mini while CoT is used for the other judges? A clearer justification for this choice would help readers understand the experimental consistency.\n5. Line 257: Does “probabilistic classifier” here refer to the fitted model? If so, it would be helpful to make this explicit.\n6. The multinomial version of the model is placed in the appendix, but given its brevity, it might be better included in the main text to maintain continuity.\n7. The implementation details of the second baseline (\"LogReg\") are unclear. How exactly is it constructed, and how does it differ from the proposed model? A more thorough comparison is needed.\n8. The caption for Figure 1 is incorrectly labeled as Table 1.\n9. According to Figure 1, many results have large standard deviations—much larger than the mean differences between LogReg and the proposed model—raising questions about the robustness of the results.\n10. Line 303: This claim that conflicts with Dubois et al. is that Dubois et al. found LLM judges favor verbosity, not that length-controlled scoring improves human alignment, so the sentence should be revised for accuracy. Furthermore, although Table 2 shows consistent trends across four models, none of them match those used in Dubois et al. To strengthen this point, it would be valuable to replicate the analysis on overlapping models.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n- This paper introduces a statistical framework to model human and LLM judgements, which enables formal tests of human-LLM discrepancies.\n- The authors evaluate the framework on two benchmarks (BigGen Bench and Chatbot Arena) and four LLM judges.\n- The authors study two applications on improving LLM judgements with few annotations and identifying biases in LLM judges.\n\nSTRENGTHS AND WEAKNESSES:\nQuality:\n- There are only 4 LLM judges evaluated in this work. It would be great to see results on the two applications on a larger set of models, especially bigger / SOTA models.\n- While it is great that the experiments are on in-the-wild data (e.g., Chatbot Arena), it is hard to know what the “ground truth” results should be. For example, with Application 2, we don’t know what the ground truth LLM biases are. It would be helpful to have some synthetic but more controlled experiments to show the effectiveness of the framework. \n\nClarity:\n- The methodology is nicely written; my main comments are on the two applications.\n- For Application 1, given the error bars, it’s not clear that the proposed methods improve accuracy. In fact, even with increasing sample size, the accuracy looks about the same. Could the authors provide a much larger sample size variant (e.g., an order or two magnitude greater) to compare against? Additionally, can the authors discuss how there might be significant variance in human annotators when given only a small number of annotations? How is that accounted for?\n- For Application 2, how do results vary based on the amount of training data? How robust are the findings of observed LLM biases? \n- Additionally, for Chatbot Arena experiments, did you filter for specific categories? The types of biases we might expect could vary depending on the topic.\n\nSignificance:\n- This work contributes to an important and growing area of using LLMs as judges. \n\nOriginality:\n- A formal approach to comparing human and LLM judges is very novel.\n\nQUESTIONS:\n- Can the authors evaluate more models as LLMs on even a subset of the questions? I’m interested in “profiling” different LLMs and seeing trends.\n- Can the authors discuss the use of in-the-wild data and its potential implications?\n- Can the authors clarify comments around both applications and their respective experimental designs?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose an ordered-logit model that ties human ratings to LLM-judge ratings through a shared latent \"human-preference\" score and a linear covariate term capturing systematic judge biases. They introduce a \"logit trick\" that recovers the latent scores without ever observing them, prove asymptotic normality for all key parameters, and demonstrate three practical uses: (i) probabilistic calibration of LLM judges, (ii) few-label alignment with humans, and (iii) hypothesis-driven detection of judge biases. Experiments on BigGen Bench and Chatbot Arena show lower cross-entropy, tighter calibration, better accuracy, and interpretable bias coefficients (e.g., length, creativity) than raw or logistic-regression baselines.\n\nSTRENGTHS AND WEAKNESSES:\nI find the paper’s overall quality and clarity strong, and I especially appreciate the rigorous derivations presented in Proposition 3.1 and Theorem 3.2. Nevertheless, I have several concerns.\n\nFirst, I am unsure how sensitive the method is to the choice of covariates. If handcrafted features were replaced by GPT-based embeddings, would the estimated γ coefficients shrink, explode, or remain stable? Quantifying this effect would illuminate how interpretable and trustworthy the bias term truly is.\n\nSecond, the discussion acknowledges that the model may break down when the latent-score assumption is violated, yet I see no empirical test of this vulnerability. Have you simulated scenarios in which the true relationship between human and LLM scores is nonlinear? A targeted robustness study would increase my confidence in the method.\n\nMy broader worry centers on the significance of this work in today’s LLM-evaluation landscape. As language models tackle ever-more complex tasks, obtaining high-quality \"gold\" human annotations is increasingly difficult, and recent studies highlight substantial disagreement among annotators on nuanced questions. In light of this, can human labels still be considered a reliable gold standard, and is narrowing the gap between humans and LLM judges still the most pressing problem?\n\nRelatedly, your approach assumes discrete rating labels. How might it extend to settings where evaluation feedback is open-ended and expressed in free-form natural language rather than fixed scales?\n\nQUESTIONS:\nSee my feedback above.\n\nLIMITATIONS:\nLimitations are addressed in the discussion and corresponding questions are asked in my review.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
The authors propose a statistical framework to align LLM agent ratings with human preferences. They model both human and LLM judgements as outcomes of a shared latent human preference signal. The LLM-as-a-judge framework is widely used and this work provides a solution to improve alignment with human judges.

STRENGTHS AND WEAKNESSES:
Strengths:
- The paper addresses a critical gap in recent research 
- The paper is well written and structured
- The authors demonstrate a practical benefit of their approach, even with only a few labeled data points

Weaknesses:
- As the authors themselves address, the model is vulnerable to misspecification
- Necessary domain knowledge makes the framework less generalizable
- Human judgement are treated as gold standard, even if they can also contain biases. However it is the goal of the paper and also in most research studies to align agent with human judgements which is clearly achieved here

Despite the presence of weaknesses, this paper makes an important contribution to the formal alignment of human and LLM judgements, with implications for various applications of the LLM-as-judge framework.

Comments:
- You can use ~ before \citet or \citep to avoid references at the beginning of a line (see line 103)
- "Table 1" looks more like a Figure to me?

QUESTIONS:
- Will your framework be publicly available after the review process? 
- Did your method reveal any patterns in which LLMs (e.g., size or training regimen) are more misaligned with humans?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03