Benchmarking LLMs' Judgments with No Gold Standard
Authors: Shengwei Xu
Yuxuan Lu
Grant Schoenebeck
Yuqing Kong
Venue: ICLR 2025 Poster (2025)
Keywords: Benchmarking
Peer Review
Mutual Information
Data Contamination
Large Language Models
Primary area: datasets and benchmarks

Abstract: We introduce the GEM (Generative Estimator for Mutual Information), an evaluation metric for assessing language generation by large language models (LLMs), particularly in generating informative judgments, without the need for a gold standard reference. GEM broadens the scenarios where we can benchmark LLM generation performance-from traditional ones, like machine translation and summarization, where gold standard references are readily available, to subjective tasks without clear gold standards, such as academic peer review.

GEM uses a generative model to estimate mutual information between candidate and reference responses, without requiring the reference to be a gold standard. In experiments on two human-annotated datasets, GEM demonstrates competitive correlations with human scores compared to the state-of-the-art GPT-4o Examiner, and outperforms all other baselines. Additionally, GEM is more robust against strategic manipulation, such as rephrasing or elongation, which can artificially inflate scores under a GPT-4o Examiner. 

We also present GRE-bench (Generating Review Evaluation Benchmark) which evaluates LLMs based on how well they can generate high-quality peer reviews for academic research papers.  Because GRE-bench is based upon GEM, it inherits its robustness properties.  Additionally, GRE-bench circumvents data contamination problems (or data leakage) by using the continuous influx of new open-access research papers and peer reviews each year. We show GRE-bench results of various popular LLMs on their peer review capabilities using the ICLR2023 dataset.

Published as a conference paper at ICLR 2025
BENCHMARKING LLMs' JUDGMENTS WITH NO GOLD STANDARD
Shengwei Xu
Yuxuan Lu
School Information
School of Computer Science Peking University, Beijing, China yx_luêpku.edue edw..ccuuedd.c.
University of Michigan, Ann Arbor, USA shengwe e umi ch. edu
Grant Schoenebeck
Yuqing Kong
School Information
School of Computer Science Peking University, Beijing, China yuqi ng. kongêpku .edu. on
University of Michigan, Ann Arbor, USA schoenebe umi ch. edu
ABSTRACT
We introduce the GEM (Generative Estimator for Mutual Information), an evaluation metric for assessing language generation by Large Language Models (LLMs), particularly in generating informative judgments, without the need for gold standard reference. GEM broadens the scenarios where LLM generation performance-frowt traditional ones, like machine translation and summarization, where gold standard references are readily available, to subjective tasks without clear gold standards, such as academic peer review.
benchmark
GEM uses generative model to estimate mutual information between candidate and reference responses, without requiring the reference to be a gold standard. In experiments
auman--nnotatedd dataset, GEM demonstrates competitive correlations with human scores compared to the state-of-the-art GPT-4o Examiner, and outperforms all other baselines. Additionally, GEM more robust against strategic manipulations, such as rephrasing or elongation, which can artificially inflate scores under GPT-4o Examiner.
We also present GRE-bench (Generating Review Evaluation Benchmark) which evaluates LLMs based on how well they can generate high-quality peer reviews for academic research papers. Because GRE-bench is based upon GEM, it inherits its robustness properties. Additionally, GRE-bench cireumvents data contamination problems (or data leakage) by using the continuous influx of new open-access research papers and peer reviews each year. We show GRE-bench results of various popular LLMs on their peer review capabilities using the ICLR2023 dataset.
INTRODUCTION
High-quality and reliable Large Language Model (L.L) benchmarks can effectively guide research, encourage innovation, monitor their advancement, and inform users of which model to use for their purpose. The importance of the final goal is underscored by the over 900k models currently available on Hugging Face, online platform for open-source LLM Various benchmarks
proposed for evaluating LLMs' ability different aspects, including ARC (Chollet (011)), HellaSwag Swag (Zellers) al. 2019), Massive Multitask Language Understanding (MMLU) (Hendrycks al. 2020), GSM8K (Cobbe et al. 2021), TruthfulQA (Lin et al., 2021), Natural Questions (Kwiatkowski et al. 2019), etc.
Most benchmarks are based on multiple-choice questions other questions with objective gold stan- dard answers, since it is easy to verify the LLMs' outputs. While they provide valuable evaluation for
Bott authors contributed equally to the paper.
Supported by United States National Science Foundation award number 2313137.
Supported by National Natural Science Foundation of China award number 62372007.

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LLMs, open-ended tasks, e.g. providing judgment about research paper, encompass a broader array of skills and require both objective and subjective reasoning. In addition, concern has been raised about data contamination (also called data leakage), where the training data contains information about the tasks in the benchmarks, as the I.LMs are pre-trained on massive online data, which is also the source of some of the benchmark tasks. LLMSs can show unreliably good performance due to data contamination (Sainz et al.) [023] Golchin & Surdeanu 2023) Oren et al., 2023). In contrast, with open-ended questions, LLMS can be asked to provide judgments about newly created content, e.g. the latest academic papers, for which LLMs have yet to index human evaluations or responses.
However, it is not clear how to automate the evaluation of subjective response quality. An added challenge is that there is no gold standard quality response with which to compare.
We would like an evaluator to have two properties. First, should be accurate and be sensitive the semantic content response. As current LLMs have already shown strong language ability, we want to focus on evaluating the semantic informativeness of candidate responses instead of their syntax or style. Second, the evaluator should be manipulation-resistan resistatttessttt--eeeee we should not be able to manipulate a response in a trivial fashion to consistently increase evaluation scores. This is important because otherwise one cannot determine whether a high evaluation score indicates that the LLM output a high- quality response or merely results from manipulation designed to achieve an artificially inflated score.
Given these gaps in previous research, research question is: Can develop accurate, manipulation-resit resistat,,,,,,,,n,,,,,. and automated evaluation metries for textual responses with no gold standard reference to compare with?
A straightforward approach may involve utilizing an alternative LLM as an oracle examiner to directly provide the evaluation, which has demonstrated efficacy in evaluating open open-response QA (Bai et al., (002)), chatbot (Zheng et al. (023), etc. However, LLM examiners can have blind spots and be susceptible to certain manipulations (Hada et al., 2023 Doddapaneni al., 2024). In our experiments, elongating all responses by adding the same fixed sentences can significantly increase the GPT-4o LM examiner's score. Interestingly, previous research has shown that even human evaluations can be manipulated by adding meaningless text (Goldberg et al. 2023). Mcanwhile, other automated natural language generation (NLG) evaluation metrics, including BLEU (Papineni et al. 2002), ROUGE (Lin) 2004). BERTScore (Zhang et al. (201)), BARTScore (Yuan et al. 2021) and GPTScore (Fu et al., 2023). rely on comparison with a gold standard reference.
Our Contributions. We propose the Generative Estimator for Mutual information (GEM) which uses the estimated Shannon mutual information (MI) between a set of candidate responses and a set of peer reference responses, which need not be of gold standard quality. The mutual information measures the amount of information revealed about the reference responses by obtaining the candidate responses. By artificially removing stylistic and syntactical information, our approach measures how much semantic information the candidate responses can reveal about the reference responses, which is related to the concept of semantic coverage (Yuan et al.) 2021: Nenkova & Passonneau 2004). We additionally propose a variant of our method, Generative Estimator for Mutual Information with Synopsis (GEM-S), which estimates the mutual information conditional on synopsis of the task, c.g. the abstract of the paper. This prevents candidate response from receiving a high score based solely on superficial information. Consequently. the score emphasizes the additional semantic information gained from the candidate responses.
For implementation, we utilize a generative language model to estimate the conditional distribution between two text responses. Thus, the GEM can be categorized as an LLM www.---rbbbbbbtt-bbbe based metric, with techniques similar to BARTScore (Yuan et al. 2021) and GPTScore (Fu et al.) 2023), from which our metric inherits the effectiveness of evaluating objective tasks with gold standard.
Our work establishes a bridge between the information theoretical framework in the literature of information elicitation without verification (Kong & Schoenebeck 2019 Lu et al. (024) and the NLG evaluation problem. Though GEM and GEM-S resemble the GPPM and GSPPM mechanisms in Lu et al. 2024), with manipulation resistance aligned to their incentive compatibility, we make a necessary change to make the score more suitable for the NLG evaluation problem. Specifically, for incentive compatibility, the score can rely solely accuracy of predicting the peer reference, while we use mutual information to capture the gain in that accuracy for evaluation purposes.
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Results Overview. The results of experiments validate GEM's accuracy and resistance to manipulation, and compare it with many different NLG evaluation metrics.
1. Positive Correlation with Human Annotation (Section
On human-annotated dataset, the GEM metrics, especially GEM-S, achieve a significant positive correlation with human-labeled quality scores and demonstrate competitive performance with GPT4-o Examiner while outperforming all other baseline metrics.
2. Better Sensitivity to Degradations (Section 4.2) Compared to various baseline metries, GEM and GEM-S are the only metrics that demonstrate significant sensitivity to all semantic degradations in our experiment, by effectively penalizing degraded responses.
3. Better Robustness against Manipulations (Section GEM and GEM-S are the only metrics that exhibit no significant score increases after meaningless elongation (Figure and HIPT-40/LJlma-33 rephrase, whereas LMExaminer show vulnerabilities by significantly increasing scores after all manipulations.
Building on GEM and GEM-S, we introduce the GRE-bench (Generating Review Evaluation Benchmark) to evaluate LLMs' peer review capabilities with data from open-access research papers and peer reviews. With the continuous influx of new data each year, GRE-bence can mitigate data contamination issues. In addition, since GRE-bench is based on GEM or GEM-S, it inherits their nice properties demonstrated in the experiments above.
We run GRE-bench for various state-of-the-art LLMs on the ICLR 2023 dataset, and present the results in Section [ We find a strong correlation between parameter size and GRE-bench score within LLM families, which further validates the effectiveness of GEM and GEM-S.
1.1 RELATED WORK
LLMs in academic peer review. Given the success of LLMs, they have the potential to assist with peer reviews when used appropriately (Robertson 2023 Kuznetsov et al. 2024 Liang et al. (2023
use a survey to evaluate GPT--4-generated reviews, and find that the GPT aPPl-generated reviews are thought helpful by more than 50% of participants. However, there have been concerns regarding issues such as hallucinations (Donker 2203) and inconsistent performance Liu & Shah (2023). While our study uses the peer review scenario to evaluate LLM capabilities, also provides quantitative comparison for the effectiveness of various LLMs in generating informative reviews.
Information Elicitation. Information elicitation employ a principal- model (Ali Silvey 1966) where the principal aims to elicit information from the agent, e.g. the probability of rain tomorrow. To incentivize the agent to provide truthful and informative reports, payment mechanisms are employed that reward truthful and informative reports more than untruthful or uninformative ones. When the information is verifiable, e.g., we will eventually know whether rains tomorrow, proper scoring rules (Good 1952, Brier 1950 Hendrickson & Buchler 1971) can be applied, which are similar to the loss functions in supervised learning. When the information is subjective and unverifiable, Miller et al. (2005) propose the peer prediction mechanism, suggesting rewarding data by how well it predicts a peer's 's subjective report which is judged by a proper scoring rule. Prelec (2004) propose the Bayesian Truth Serum mechanism. Kong & Schoenebeck (2019) propose an information-theorrtic framework, suggesting paying the agents according to the f-mutual information between their reports. The framework provides unified theoretical view of previous mechanisms in Dasgupta & Ghosh (2013), and Prelec (2004). Lu et al. (2024) first generalize the peer prediction to elicit informative textual responses with LLMs, providing the foundation for our GEM metrics.
2 PRELIMINARIES
Model. We consider scenario with candidate under evaluation and peer reference working on the same tasks. We denote W as the space of all tasks, with an underlying distribution over it, denoted as AW. Given a sample task W ~ AW, both the candidate and the reference generate response, represented by the random variable X and Y respectively. The response space for both X and Y is denoted by Σ. The tuple (W.X,Y) follows underlying distribution D == A(Wx E²). Note that the reference's 's responses do not need to be gold standard. We assume that X and Y are independent conditioning the task W.
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Empirically, in dataset D, have a list of sampled tasks {wi, ,w,,,, and list of corresponding reference responses y= (y/.-r.un Let the candidate generate a list of responses x= .. to the corresponding tasks. We adopt a common assumption that all the samples (W,,0i;)e) are generated i.i.d. following the underlying distribution DD
Benchmarking the candidate. The evaluation metric f T:Wx x E² - R maps tuple (w,z.y) to score S. Note that some evaluation metrics only utilize one or two of the items in the tuple. the evaluation metric utilizes language model compute the score, we call the model used as the evaluatiinn-LM. Different evaluation-LMSs may lead to different evaluation results. A nice property of our GRE-bench is the consistency over evaluation LMs, i.e., using smaller evaluation-La will not significantly change the evaluation results, we will discuss it in Appendix[A.] Given a dataset D and an evaluation metric f. we can estimate www.).y)ll((WW... the evaluation metrics over all samples, which is named the benchmark.
by using the average of
Shannon Mutual Information. Ideally, we aim to score the candidate according to the informative- ness of her responses, thus, Shannon Mutual Information (MI) (Shannon [948) is suitable metric. The MI between two random variables, and Y. provides a quantitative measure of the information shared between them. Specifically, it assesses how much knowing one variable can inform us about the other. This is formally defined as: I(X;Y)= En,,, Pr[X =, = -yllog Pr|X=a,Y=
Pr[x PPrPr (r The conditional mutual information T T(X Z) measures the mutual information between X and Y,
conditioned on random variable 7: T(X;Y ( = Pr <]x(X
In an academic peer review scenario, Z can be some superficial information of a paper (e.g. the abstract), the conditional MI measures how much information revealed beyond the abstract and emphasizes the MI gained from the semantics judgment.
Estimator. To estimate the MI with samples, we employ Point-wise Mutual Information (PMI) (Fano & Hawkins 1961 Church & Hanks 1990). an unbiased estimator of the MI. Formally, the PMI between realization x and y is defined
Point-wise Mutual Information
=x(X=x,Y=-))
PMI(2:) : Pr X = _rrrrrrrP---- =w] logPr[( = X x -lggrr
Similarly, the PMI between and conditional on is defined as PMT(a; =logP/r =y x=
logP Y Z=
GENERATIVE ESTIMATOR FOR MUTUAL INFORMATION (GEM)
In this section, we propose the Generative Estimator for Mutual Information (GEM), prove its theoretical effectiveness even the reference's responses are not gold standard under certain assumptions, and discuss its empirical practice.
As discussed in Section 2, we use the mean of sample PMIs as an unbiased estimator of the MI. Given sampled tuple with task wy, a candidate response and a reference response y/, aim to compute PMI(a:y;) =logPr(Y= X -
Following Yuan et al. ) Fu al. 2023 Lu et al. (0024), we generative language model to estimate the probability distribution of the reference response conditional on the candidate's output, denoted by PrL.M ==x). and the marginal distribution, denoted by PILlM =y]-
Specifically, we use the LLMs' inherent token prediction function. We tokenize y as (y)) we use the LLM to predict PLeIM[ (k++)) (k++)) (((+))((()).......)))))))))))))) ( (y)")... (x)_ x) With Bayesian rule, by multiplying over k, we have
and then integrate æ into the prompt. Given any length-k prefix (.))
ly]
Pr Y X= II L.M
Pr (k+1)) xy | (1))... = yy (.... x=x)..
L.L.
k=
Similarly, we can estimate the marginal distribution logPrT..MYY Y= Thus, we have the estimated PMI, denoted as =lok::))looPPLMM.. = X= llgP = w]. and consequently, the estimated MI over dataset D is - PM
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3.1 THEORETICAL GUARANTEES: BENCHMARKING LLMS WITH NO GOLD STANDARD
We now show that even when the reference is not gold standard, the estimated MI can still be a benchmark where better candidates achieve higher scores theoretically. We adopt widely-used model in decision-making theory [Blackwell] 1951 1953). The candidate's 's response follows an information structure, mapping a: W + AΣ, where > the response space. It represents the distribution of responses conditional on the task.
Within this model, Blackwell's order (Blackwell, 1951 1953) provides a partial order of the informativeness of information structures. Consider two candidates, H and L, whose responses X and X, follow information structures σн and dt, respectively. Information structure dy more informative than or. in the sense of Blackwell order if there exists stochastic mapping I, such that 0r, Tow Intuitively, the information structure with lower Blackwell order more noisy, and thus, can be regarded as lower quality.
We now show that higher Blackwell order information leads to (approximately) higher GEM scores when the LLM can provide a "good" estimation. Formally, we have
Proposition 3.1. When the KL-divergence between the LLM estimated distribution and the underlying distribution satisfies
DEL Pr Px= IxY=-(Xm===) <c, VEH- LLLM
For the two candidates H and I. discussed above, the information structure of H Blackwell dominates L's, l's, when the size of dataset n. goes infinity, we have I(X (at::Y2>TXXXX>) I(Xx;Y))-
The proof is provided in Appendix B following previous information elicitation literature (Lu et al. 2024). mainly relies on the data processing inequality (Shannon 1948] Thomas & Joy (006). Ad- ditionally, we provide similar proposition for conditional MI in Appendix We highlight that while this theoretical result requires a strong assumption, it gives intuitions why and how our GEM scores could work. Real-data experiments are then conducted to further validate the effectiveness of GEM.
3.2 FILTERING OUT SHORTCUTS
Textual data is inherently high-dimensional,, encompassing various aspects. For example, peer reviews may have hicrarchical information structure, including language style, the topic of the paper, and critical judgments, as shown in Figure While each of these dimensions influences LLM predictions and consequently contributes to the mutual information, they are not equally important when assessing the quality of --Leerrerrteed responses, particularly in the context of peer reviews.
Empirically, the correlation between superficial information can act as "shortcuts" (Geirhos et al. 2020) confounding L.M predictions, cause unintended bias in the estimated mutual information, and consequently distort the evaluation metric towards superficial correlations rather than thorough judgments. Since we focus on the semantic quality of these responses, we aim filter out dimensions like language styles that could skew the evaluation metric by offering "shortcuts" in LLM predictions.
Thorough
Critical judgmeetts
GEM-S FANSW) PAICk: (asse)
GEM-S fabstrach: P1
westnesses
Topic Keysonnde.. zovered in abstraut
PRICE:P)
Language style Fornut
SuperGeinl
Figure Example Hierarchical Information Structure in Peer Reviews
Preprocessing. A straightfforwar yet effective method proposed by Lu et al. 2024). is preprocessing responses using a specific LLM to rephrase and summarize the content. This preprocessing step stan-
"The -ivveeegee between two distributions over the same probability space is DxL (PIIQ) Σ, (()))o(((()))))
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dardizes language style and eliminates superficial information, thereby reducing the influence of irrele- vant dimensions. We denote the preprocessed versions of responses 2; and Yi as é; and yi respectively.
Dataset
Preprocessing
Tokenization
LIM Prodietioo
Caudidax
Staddarty Rephrased
Pr
Repuese
Refecerr Recpatic
Steedarthy Rophraund
Tb.
Estimated MI: GEM(D) E PM
log Pr
Figure 2: An Overview of Our Generative Estimator for Mutual Information
We have now outlined all steps of our Generative Estimator for Mutual Information (GEM) framework, as illustrated in Figure The GEM metric between a candidate response iy and reference response is defined follows:
GEM(Z..)::
By taking the arithmetic average of all GFM( over the dataset D, we have estimated mutual information between random variables and Y, as shown in Figure
For comparison, we also introduce the GEM-raw metric calculated without preprocessing follows: CFM-raw(a..J/) (M,()) which serves as baseline.
GEM-S: conditioning out the synopsis. Kong & Schoenebeck (2018) suggest surface-level information can inflate mutual information through simple correlations, reducing the mutual information's 's effectiveness in reflecting semantic informativeness, especially when the responses have hierarchical information structure as shown in Figure [] As some superficial information can be contained in a synopsis of the task, e.g. the abstract of the paper to be reviewed, conditional mutual information can be used distill the semantic informativeness (Lu et al. 2024).
The GEM-S metric is designed to mitigate this issue by conditioning out the synopsis of the task, denoted as (w:)). The GEM-S metric formally defined as:
(FMMS(0((s)).aa:)))) = :D)))PMMMii (A. | (((w)))
With the conditional formulation above, only the additional, non-trivial mutual information is measured, thereby offering more accurate assessment of the candidate's performance in providing information beyond superficial content.
Moreover, adjust the granularity of the synopsis to make the GEM-S metric focus evaluating different aspects the candidate LLMs' ability. In the example of Figure [ the standard GEM measures the general ability to produce relevant reviews. By conditioning on the abstract, GEM-S (abstract) evaluates the LLM's ability to generate judgments, including retrieving author-stated strengths and weaknesses (ASSW) and providing critical feedback. By further conditioning summary of these author-stated strengths and weaknesses, GEM-S (ASSW) narrows the focus to the LLM's capacity for critical thinking and delivering constructive feedback.
EXPERIMENT: VALIDATING GEM's EFFECTIVENESS
In this section, we provide our experiment setup to validate the effectiveness of the GEM metric in benchmarking LLMSs without gold-standard references, and show the results. Code repository at htttp:://itthb..omm httphh..com//y.. lu/ Benchmarking LLMM-- Judgme--sudgmmttt----ii.. No Noooooo--Saaaaaa.-c..
GEM setup. We present the empirical setup of the GEM metric and its variants. In the validation ex- periment in this section, Llama-3.1 8B is used as the evaluation-LM to estimate logPrLLM -r=yylyyll X x] and logPPLLM[** [Y y]. After confirming the effectiveness GEM metrics using the small model, for even better performance, ing the GRE-bennc on the ICLR 2023 dataset in Section S We show the correlation between results of the smaller (8B) and larger (70B) models in Appendix A.2 to ensure robustness of our approach.
scaled up larger, 70B parameter version of 3.ama-3.1 for comput-

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For text preprocessing, employ GPT-4o. We also present the results with Llama-3.2 90B prepro- cessing in Appendix A.33 and demonstrate the robustness of GEM metrics with various preprocessing models. For GEM-S, without further indication,
the abstract of the paper as the synopsis in this section. The prompts for probability estimation and preprocessing can be found Appendix C
Baselines. We compare the performance of GEM and its variants with the following established evaluation metrics commonly used in NLG evaluation, including two metrics from the pre-LLM era, BLEU (Papineni et al.) 2002) and ROUGE-L (Lin) (004),, and embedding-basee BERTScore (Zhang et al., 2019). probability-basee
metric,
metric BARTScore (Yuan et al., (02T). and
metric LMExaminer (Language Model as Examiner) (Bai al. 2024). The detailed
implementation of all baselines is provided in Appendix D.I
4.1 RESULT 1: POSITIVE CORRELATION WITH HUMAN ANNOTATION
In this section, we show the experiment results utilizing a well the GEM metrics correlate with human-labelee quality scores compared to various baselines. higher correlation indicates that the evaluation metric better aligns with human preference.
dataset to validate how
Note that, perfect correlation can be impossible to achieve for several reasons: Human annotations are inherently noisy (Goldberg et al., (023), evaluation of individual responses can also be noisy due to subjectivity,, and there is no gold-standard reference. Nonetheless, our goal is to demonstrate systematic-level positive correlation.
Score. For task w; with corresponding responses x; x (mig) where Taj is the j-th response. The score of response Zij is computed by |x| - (WNg where f is the evaluation metric.
Gluman-Annotate Peer Grading Dataset. This dataset contains 30 student project proposals in graduate-level class on machine learning and about 180 peer evaluations the proposals. We use GPT- 4o generate short abstract for each proposal. Each proposal has least 4 reviews, and each review contains an overall score and textual feedback, including "Strengths of the project", "Weaknesses of the project/likely roadblocks", and "Ideas for improvement or specific directions". According the quality of students' textual feedback, an instructor manually grades the reviews with grade A, B, or C.
Evaluation Metrie Spearman's p p-value
Evaluation Metric
Spearman's p p-value
BLEU ROUGE-L
0.023
0.772
BARTScore-recall
0.164
0.036
-0.244
0.002
LMExaminer
0.537
1.le-13
BERTScore
-0.061
0.439
GEM-raw GEM
0,300
9.2e-05
BART Score-F -0.237
0.002
0.431
7.5e-09
BARTScore-pree. -0.511
2.3e-12
GEM-S
0.479
7.4e-11
Table I:
Spearman's correlation coefficient (p) and p-values between various evaluation metrics and instructor andottaedd grades. Significant positive correlations (p (p< 0.05) are bolded.
Statistics Metric and Results. Table shows Spearman's 's correlation coefficient p between the scores of various evaluation metrics and the instructors' grades. All variants of our GEM metrics show a sigmificant positive correlation. The GEM-S has the highest correlation over all three GEM variants, implying the effectiveness of filtering out shortcuts. It also shows competitive performance compared GPT-40 LMExaminer which has the highest correlation. All other baselines perform worse than GEM, and only LLRTScore-RReall has significant positive correlation. In later sections, we provide further comparisons between GEM and LMExaminer.
4.2 RESULT 2: BETTER SENSITIVITY TO DEGRADATION
In this section, aim to compare how sensitive the evaluation metrics are to degradations that obvi- ously reduce the semantic informativeness indicates the sensitivity of the metric. We first introduce our validation workflow in Algorithm the ICLR peer review dataset, and then introduce several degradation strategies. The workflow and dataset will also be used in the robustness check again manipulation strategies in Section 4.3
the responses. A statistically significant score decrease
The validation workflow utilizes a dataset D consisting of n tuples (w.).,.3)) of tasks, candidate responses, and reference responses. For each tuple, first compute the score s;:= ((,....,))).
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We then replace the response 2; with z; according degraadation/mmniillatooo strategy M, and compute s/ : f f(,,, Finally, we compute the means p.te and standard deviations o a.0' of (st and { (ij)) respectively. A formal workflow is provided in Algorithm 1 of Appendix D.2
Note that the evaluation metrics that we compare are in different scales, thus, instead of comparing the mean difference, we standardize the scores with pooled standard deviation ppoled := (
and compare the standardized mean difference (SMD) d SMD is also known as Cohen's 's (Cohen 1988).
Andrade 2020). This form of ppeted
ICLR Dataset. We use the ICLR 2023 peer review data publicly available OpenReview. We ran- domly select 300 papers as our benchmark dataset, and for each paper, we randomly select 3 original human reviews: one review serve human candidate, and the other two reference responses.
Degradation Strategies. We introduce three degradation strategies as follows. Note that the degraded responses after Sentence Deletion or Deletion & Completion be regarded as Blackwell-dominate by the original one. The detailed setup is provided in Appendix D.3
1. Sentence Deletion. We delete every other sentence of to get degraded response x!.
2. Deletion & Completion. After deletion, we use GPT-4o to complete the deleted sentence with consistent language style and semantics to the original response, which provides an informatively degraded response 2! but with similar length to the original one.
3. Abstract-only Review. We use Claude-3 sonne to create fictitious review d/ with only the abstract of the paper, and use x to replace 24.
Statistics and Results. We compare the standardized mean differences (SMD) of evaluation metrics in Table GEM and GEM-S are only two metrics that consistently have significant negative SMD on all three degradations. The LMExaminer shows the highest effectiveness on Sentence Deletion and Deletion & Completion, but failed to penalize reviews only based on the abstract.
Evaluation Metric
Sentence Deletion
Deletion & Completion
Abstract-only Review
GPT-4o Rephrase
Llama3.1 Rephrase
Meaningless Elongation
BLEU
-0.282
0.016
-0.692
-0.975
-0.920
-0.165
ROUGE-L
-0,073
0.091
0.022
0.028
0.120
-0.196
BERTScore
-0.100
-0.188
0.840
0.134
0.063
0.064
BARTS.-FI
0.401
0.201
0.394
0.130
0.332
-0,304
BARTS
0.627
0.317
0.617
0,218
0,541
-0.439
BARTS.-recal
-0.017
-0.016
0.004
-0.027
-0.028
0.004
LMFxaminer
-1,290
-0.417
0.715
0.187
0.104
0.105
GEM-rav -0.123
-0.126
0.020
-0.123
-0.126
0.020
GEM
-0.401
-0.308
-0.191
-0.058
-0.107
-0.063
GEM-S -0.409
-0.206
-0.566
-0.046
-0.114
-0,070
Table 2: Standardized Mean Differences (SMD) d: (z==(=--)) pooled of scores after degradations and manipulations various evaluation metrics. Significant score decreases (p<c 0.05) after degradations highlighted in bold green, implying the evaluation metric can effectively penalize the degradation. Significant score increases (p 0.00)) after manipulations are highlighted in bold red, implying the evaluation metric is robust against the manipulation. We provide 95% confidence interval of SMD values in Appendix A.I
4.3 RESULT' 3: BETTER ROBUSTNESS AGAINST MANIPULATION
Evidence has been found that the LLM evaluators, and even humans, are not robust against specific manipulation strategies. they may rely on language style or length as shortcuts instead of providing an evaluation based on semantic quality (Panickssery et al., 2024, Bai al., 2024 Goldberg et al.)
2023 thus, it is important to further check our metric's robustness against several manipulations.
the time our experiment, ICLR 2024 review data was not yet available through the OpenReview API. WWe intentionally use different I.LM from Deletion & Completion make sure the effect of score decrease not caused only by the L.M.
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Figure 3: Meaningless Elongation
Specifically, we employ two manipulation strategies, which do not significantly change the semantics, but may change the language style or add meaningless starting sentences. After manipulation, if the score significantly increases, the evaluation metric fails to pass the robustness check. We use the same workflow and dataset introduced in Section 4.2
Manipulation Strategies. We now introduce the manipulation strategies. The detailed setup, including the prompts and examples, is provided in Appendix D.4
1. GPT-4o/Llama-3.1 1 Rephrase. Following Bai et al. (0224). we use GPT-4o and Llama-3.1 to rephrase the response r, while trying to keep the semantics unchanged. We test whether the evaluation metrics have preference towards GPT-4o or Llama-3.1 generated language.
2. Meaningless Elongation. We adopt a meaningless elongation method similar to Gooldberg et al. (2023)'s 's human subject experiment where set of fixed meaningless sentences is added to each section of all the reviews as shown in Figure 3
Statistics and Results. The SMDs shown in Table demonstrate that LMexaminer has significant score increases after all three manipulations, indicating its lack of ctaipppuaatoorrssstaan In contrast, GEM and GEM-S exhibit no significant score increase across these manipulations. Furthermore, comparing SMDs after degradations and manipulations in Table we observe that the scores of GEM and GEM-S decrease less after manipulations compared to degradations, indicating that manipulations result in less information loss than degradations.
5 GRE-BENCH: BENCHMARKING LLMs WITH GEM
After validating our evaluation metrics GEM and GEM-S in Section 4.1 4.2 and 4.3 we now show the results of GRE-bench (Generating Review Evaluation Benchmark) based on GEM and GEM-S. We use the same ICLR 2023 peer review dataset introduced in Section 4.2
We prompt LLMs generate detailed reviews for each paper in the dataset, with the prompt inspired by Liang et al. 2023). which covers various aspects, including paper summary, strengths, weaknesses, questions, etc. Details of the prompts are provided in Appendix C.3
We then use GEM and GEM-S to score the LLM-generated reviews. Specifically, as discussed in Section 3.2 we use GEM-S (abstract) and GEM-S (ASSW), where the synopsis consists of an abstract for GEM-S (abstract), and an abstract supplemented with a summary of author-stated strengths and weaknesses (ASSW) for GEM-S (ASSW). We prompt GPT-4o to get the ASSW summary of each paper, the prompt is shown in Appendix C.4 We visualize the results of the GRE-bench based on GEM, GEM-S (abstract), and GEM-S (ASSW) W) respectively in Figure More detailed numerical results are provided in Table of Appendix A.2
Figure shows that, within each LLM family, such as the Llama-3 or Claude series, models with larger parameter sizes tend to perform better. Moreover, newer versions tend outperform their predeces- sors with similar model sizes, as seen in comparisons between Llama-3.1 and Llama-3. These observa- tions further validate the effectiveness of our GEM and GEM-S metrics in accurately evaluating LLMs.
Notably, several models with large parameter sizes surpass the human baseline (gray line) when using GEM and GEM-S (abstract) metrics. We hypothesize this is because strong L.LMs like Claude-3 opus

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Published as a conference paper at ICLR 2025
Cleude-3-opus
Sonnet
Claude hehkk
Claude 2
10
Gemini 1ro-1.5
Gemini-pre- pro-i
GPT-4o
GPT turbo
GPi--
Deepseek-v3
Parameter
Deepseckrr
Uama-3 1-70b
Liama-3.1:00
Uame-3-70b
Lame 3
Mistra-Harge
Mistral-small
Mistral-tiny
103e
Mixtral 8x22b
10
15
20
25
6
12
6 GEM-S (ASSW)
4
GEM
GEM-S (abstract)
Figure 4: Results of GRE-bencis based on three evaluation metries with 90% confidence intervals vs. model parameter size size: Indicated by color. The grey line represents the average human baseline, with the 90% confidence interval shaded in grey.
effectively retrieve contributions and limitations stated by the authors, and will include that in their reviews. In contrast, human reviewers tend to omit judgments that are less important or already stated by the authors due writing costs, resulting in a loss of mutual information. This hypothesis is supported by the observation that, when we condition out the author-stated strengths and weaknesses, the human reviewer has the highest GRE-bench score based on GEM-S (ASSW).
3.2 This observation further validates the hierarchical information structure discussed in Section 3.2 By conditioning out different levels of information, the benchmark captures subtly different abilities. GEM-S (ASSW) emphasizes the quality of critical judgment, while GEM and GEM-S (abstract) focus more on evaluating LLMs' foundational abilities, including retrieving useful information from the paper, which is especially valuable for differentiating LLMs with smaller parameter size.
In Appendix A.2, we provide more detailed results and discussions of GRE--bench, including GRE- bench's 's correlations with other benchmarks, its consistency across evaluation LMS (Llama-3.1 8B and 1 70B), and detailed numerical results.
6 CONCLUSION AND DISCUSSION
In conclusion, we introduce GEM and GEM-S, two metrics for evaluating LLMs on tasks without gold-standard references. They have proved to be accurate and cumipulation-resistaan outperforming other metries in the experiment. Based on GEM and GEM-S, we present GRE-bench for assessing L.MSs peer review capabilities while minimizing the risk of data contamination.
Our approach has certain limitations. First, although our experiments demonstrate its effectiveness, the theoretical results depend on the accurate estimation of the underlying distribution by the LLM, which may not always be reliable. With ongoing advancements in LLM technology, we hope that these estimations will become more accurate over time. Second, while our method does not require information. If
gold-standard references, it requires reference that includes original all reviewers submit solely LLM -eMegeerateed reviews- such as those only from GPT-4o the mutual information- based approach may unfairly assign the highest score GPT-40. This echoes recent research demonstrating that the absence of original human-generate data in Al evaluation can result in model collapse (Shumailov et 2024).
For future work, we can use new open-access peer review data to update GRE-bench every year and observe how the evaluation evolves over time, to monitor any improvements in LLMs' peer review capabilities. This will also ensure the benchmark stays current and minimizes data contamination. Additionally, exploring GEM's potential evaluate subjective content in other scenarios, such as book reviews and movie reviews, could broaden its applicability and further demonstrate its versatility. We provide initial results validating GEM on a Yelp restaurant review dataset in Appendix A.4
'In Figure the parameter sizes of closed-souree models estimated from unofficill sources. This information is only provide context, and of our conclusions key findings depend on these estimates.
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ACKNOWLEDGMENT
We would like to thank Sanzeed Anwar from University of Michigan for data processing. We also thank Zebang Xu and Tianzi Liu from Comeell University for their valuable consultation the statistical methods employed in our experiments. Additionally, we appreciate the insightful feedback and suggestions provided by the anonymous reviewers ICLR 2025.
REPRODUCIBILITY STATEMENT
In this section, we demonstrate the efforts we have made ensure better reproducibility of the paper.
Complete Theoretical Proof In Appendix B we provide a full proof of the conclusions claimed the paper.
Ready-to-Use Code We have made all the code for data collection, model fine-tuning, metric is at wwwpddddeeeetttwtt-------------e
com/ Additionally, to address potential hardware issues for reproducing the results, our code can be deployed on Google Colab (requiring a T4 higher GPU, with A100 being recommended), Experiments with Llama-3.1 1.18BB as the evaluation LM are conducted on Google Colab instances with NVIDIA A100 GPU. Experiments in Section SectionnJemmpp employ Llama-3.1 3.1-70B as the evaluation LM requiring more GPU VRAM, thus, we use rental instances with configuration of two NVIDIA H100 GPUs.
computation, and experiments available on GitHub. The Dxx-l/Benchhan
Clear Implementation and Experimental Details In Appendix C we thoroughly discuss the implementation of our GEM and GEM-S metries. In Appendix [ we provide detailed explanations of the experimental procedures, including how we degrade and manipulate reviews, as well as the methodology for computing the baseline metrics.
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A ADDITIONAL RESULTS
In this section, we provide additional detailed results that were omitted from the main paper and present the results of additional experiments further validate our GEM metries.
A.1 RESULT AND 3 WITH CONFIDENCE INTERVALS
We present the SMDs with 95% confidence intervals of various evaluation metrics under degradations and manipulations in Table 3
and
Evaluation Metric
Sentence Deletion
Deletion & Completion
Abstract-only Review
BLEU
-0.282
0.016
-0.692
(-0.346,-0.218)
(-0.013,0.044
(-0.778.-0.607)
ROUGE-L
-0.073
0.091
0.022
(-0.122,-0.025)
(0.062,0. 121)
(-0.054.0.0%
BERTScore
-0,100
-0.188
0,840
(-0.131, -00069)
(-0.222,-a) -0. 155)
(0.769,0.99 0,,9
BARTScore-F1
0,401
0.201
0,394
(0.380.0.422))
(0.184,0.218)
(0.344.0 0.445)
BARTScore-precision
0.627
0.317
0.617
(0.595,0.659)
(0.292.0.343)
(0.536.0.698)
BARTScore-recall
-0,017
-0.016
0.004
(-0.019--0.016)
(-0.017,-0.011
(0.001.0.007)
LMExaminer
-1,290
-0,417
0.715
(-1.343,-1.233)
(0.4472.003663)
(0.630,0.799)
GEM-raw
-0.123
-0.126
0.020
10.126,-0.120
(-0.132,-0.12) 121)
(0.017,0.023)
GEM
-0.401
-0,308
-0.191
-0.355.-002258
(-0.261. -0.122)
GEM-S
-0.409
-0,206
-0.566
(-0.455,-0.362)
.0.254,-0.1558
(-0.639,-0.ck -0.492)
Table 3: Standardized Mean Differences (SMD) d:= (1'-=/) /ppoled of scores after degradations of various evaluation metries with 95% confidence intervals in parentheses. Significant score decreases (p < 0.05) after degradations are highlighted in bold green, implying the evaluation metric can effectively penalize the degradation.
Evaluation Metric
GPT-4o Rephrase
Llama3.1 Rephrase
Meaningless Elongation
BLEU
-0,975
-0,920
-0.165
(-1.020.-0.9300
(-0.971.-0.87)
(-0.230.-0.101)
ROUGE-L
0.028
0.120
-0,196
(0.009.0.047)
(0.080.0.160))
(-0.241.-0.151)
BERTScore
0.134
0,063
0.064
(0.1 13.0.1155)
(0.032.0.003))
(0.042.0.086)
BARTScore-F1
0.130
0,332
-0.304
(0. 10.00, 140)
(0.299.0.38 (,365
(-0.308,-0299))
ScrTecooe-precision
0,218
0.541
-0,439
(0.204.0.233))
(0.490.0.59)
(-0.446.-0.433)
BARTSccoc-rreaal
-0.027
-0.028
0.004
(-0.028.-0A. -0,027)
(-0.029.-0.0) -0,027)
(0.004,0) 0.004,0.005.
LMexaminer
0.187
0.104
0.105
(0.153,0.221)
(0.060.0.147)
(0.069.0.1
GEM-raw
-0.
-0,126
0,020
(-0.126.-0.1 120)
(-0.132,-0.121)
(0.017,0.023)
GEM
-0.058
-0. 107
-0.063
(-0.090.-000228
(-0.143,-0.070)
1.0.097--00330
GEM-S
-0.046
-0.114
-0.070
(.0.079.-0.013)
(-0.149.-0.088)
(-0.104--0.03))
Table 4: Standardized Mean Differences (SMD) of scores d:
d==(p'' (1' (4) pooled after manipulations of various evaluation metrics with 95% confidence intervals in parentheses. Significant score increase (p < 0.05) after manipulations are highlighted in bold red, implying the evaluation metric not robust against the manipulation.
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A.2 DETAILED RESULTS OF
GRE-bench vs. Other benchmarks The ability to generate informative reviews relies on several key factors, including logical reasoning, critical thinking, and fact-checking. Various widely used benchmarks measure different aspects of LLM performance. To better understand which abilities are most critical for generating high-quality reviews, in the sense of GRE-bench scores, we compared our results with several other benchmarks.
We present the Spearman's 's correlation coefficients between the GRE-bench scores of all the models and their corresponding scores on other benchmark in Table 5 providing insights into which factors most strongly influence review generation quality. Notably, GRE-bench highly correlates with HellaSWAG and ARC-C which measures reasoning ability, and less correlates with benchmarks for coding ability (HumanEval) or math ability (MATH, GSM8K).
Base Metric
MMLU
ARC-C
HellaSwag
GSM8K
MATH
HumanEval
GPQA
GEM
0.55
0.68
0.74
0.58
0.37
0.36
0.60
GEM-S (abstract)
0.66
0.70
0.82
0.73
0.43
0.43
0.67
GEM-S (ASSW)
0.68
0.78
0.84
0.73
0.43
0.48
0.71
Table 5: Spearman's 's correlation coefficients between GRE-bbench and other popular benchmarks.
Consistent results over various Evaluation LMs. The Spearman's 's correlation coefficients between the GRE-bench scores based on GEM metric computed using Llama-3.1 8B and that same score computed with Llama-3.1 70B is 0.93. This indicates that the score is largely robust with respect to the specific evaluation LM used. The analogous coefficient for the GRE-bench scores based on GEM-S (abstract) and GEM-S (ASSW) are 0.92 and 0.90 respectively. We further provide the comprehensive results of GRE-bench with Llama-3.1 8B Table 77 This consistency shows that with metrics, small model with 8B parameters that be deployed a single NVIDIA L4 GPU, plus proper preprocessing, is still effective in evaluating the quality of judgments generated by much larger models, though larger evaluation LM may have better evaluation performance.
Numerical results. We present numerical results of various language models on the GRE-bende based on GEM, GEM-S (abstract), and GEM-S (ASSW) computed with Llama3. 70B as the evaluation LM, alongside their estimated parameter sizes in Table Note that some estimations of the parameter sizes are from unofficial sources. This information is only to provide context, and none of our conclusions depend on these estimates. The corresponding results of GRE-bench computed with 1 8B shown in Table
'We collect benchmark data from online sources, such as technical reports of language models. Since some models have missing results for specific henchmarks, exclude those entries when computing correlation. For more details, please refer our code repository.
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Model Name
Para. Size GRE-hench (in Billion)
GEM
GEM-S(abs.) GEM-S(ASSW)
deepssekrdeepseec-ccatt
671
19.27
6.88
1.74
deepseck/deepseek-11
671
17.28
7.23
1.94
openai/gpt-4o-minn (v 20240718)
~ 8
16.31
5.25
0,52
openai/gpt-40 (v 20240513)
200
20.08
7.62
2.82
openai/gpt-3.5-turbo (v 20240125)
20
19.28
6.31
1.40
anthropic/claude-2
20.04
7.17
2.27
anthropicdclande--SHiiku (v 20240307)
~ 20
20.32
7.63
2.50
authropiccclaude---ooneee (v 20240229)
70
24.11
9.53
4.25
authropiccclaude-3-oppss (v 20240229)
2000
25.77
11.52
5.89
google/gemini-pro 002)
20
14.73
4.09
-0.66
google/geminn-pre-1555 (v 002)
22.76
9.61
4.47
mocte-llma//llaa--------imnrtt
8
16.68
5.81
1.07
mntelllaan//lamaa33--------nntt
70
18.35
7.23
3.37
mistrala//mistrrllk/777-mmsuuuuu 0.1)
56
16.86
5.22
1.23
maitrahi/mixrral-kk22--nnsuuuue
metalllama//lmma-33---------ts 0.1)
18.94
5.82
1.64
8
18.74
7.60
2.40
meta- 1-tp://lammm-----------------
70
19.95
8.00
3.47
mistralai//misttalllaaggg (v 2407)
123
20.40
7.44
2.72
mistralai/miittrll small (v 2409)
22
18.08
6.70
1.28
mistralai/miittrlliinyy (v mistral-7b-0.3)
17.21
4.11
0.15
Table 6: Results of GRE-bench based on GEM, GEM-S (abstract), and GEM-S (ASSW) with Llama3.1 70B. In the column parameter size, the symbol ~ indicates that this size estimated. For mix-of-expert (MoE) models, we show the total parameter size instead of the activated parameter size.
Model Name
Para. Size GRE-bench (in Billion)
GEM
GEM-S(abs.) GEM-S(ASSW)
deepssek/deepssee-ccatt
671
47.51
17.40
11.98
deepseek/(leepseek-rl
671
47.45
18.89
13.80
openailgpt-44-mmni (v 20240718)
~ 8
42.55
14.44
10.25
openai/gpt-4o (v 20240513)
200
48.01
18.73
12.78
openai/gpt-3.5-turbo (v 20240125)
20
45.62
15.86
10.72
anthropic/claudd-2
55.94
24.29
17.22
amthropicddlannd---Hiiku (v 20240307)
~ 20
49.77
19.58
13.34
anthropic/claudee3-sonnee (v 20240229)
70
$6.49
24.16
16.52
anthropic/claudee--opuss (v 20240229)
2000
60.94
27.94
20.21
google/gemini-prr (02)
20
40.65
12.86
9.49
google/gemini-pro-15555 002)
175
52.94
22.43
16.06
mataelllmma/llmaa-------isnnn
8
43.50
15.49
11.24
70
46.39
18.60
13.68
mistrlai/mixtraa-kk777-inssuu (v 0.1)
56
45.18
15.61
11.25
mistelai/miixtralkk222--nnnnn (y 0.1)
47.29
17.03
11.85
meda-laana/llmmae3-------nnnnt
8
46.51
18.33
12.69
70
48.29
19.59
13.91
mistralai/miittrll-lagg (v 2407)
123
47.84
17.84
12.52
mistralai/mmistral-ssmos (v 2409)
22
45.01
15,90
11.10
mistralai/miittall-imi (v mistral-77-0..3)
7
43.90
13.34
9.94
Table 7: Results of GRE-bench based on GEM, GEM-S (abstract), and GEM-S (ASSW) with Ilama3.1 In the column of parameter size, the symbol indicates that this size estimated. For mix-of-expert (MoE) models, we show the total parameter size instead of activated parameter size.
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A.3 CONSISTENT RESULTS OVER PREPROCESSING MODELS
To answer the question "Does using an open-sourced preprocessing model (rather than GPT4o) still lead effective GEM metric and similar GRE-bench results?", we use Llama3.2-90B- 1-a....----iiii Visiin- Instruct model for preprocessing and re-run all validating experiments mentioned in Section 4 We find that most experimental results remain valid except for two manipulation tests. The GRE-bench results also remain highly consistent with the results of using GPT4o preprocessing.
Positive Correlation with Human Annotation GEM still shows a significant positive correlation with human annotation in the proposal peer grading dataset (Section 4.1 however, the Spearman's correlation coefficient between GEM scores and instructor grades drops from 0.433 (GPT4o preprocessing) to 0.274 (Llama3.2-90b preprocessing).
Sensitivity to Degradation In the degradation experiment (Section 4.2 preprocessing also shows significant sensitivity all degradations and even performs better in "sentence deletion" than using GPT4o to preprocess.
GEM with 1lama3.2-90b
Evaluation Metric
Sentence Deletion
Deletion & Completion
Abstract-only Review
GEM
-0,566
-0.317
-0.078
(Llama Preproccessing)
(-0.625,-0.507)
(-0.371,-00200) -0,205
(-0.154--0.002)
GEMS
-0.549
-0.327
(Llama Preproccessing)
(-0.607,-0.491))
(-0.258,-0.153)
(-0.399,-0.254)
Table 8: Standardized Mean Differences (SMD) d:= (j²' (/)/-/)/// p of scores after degradations of GEM and GEM-S metries (preprocessing with Llama-3.2 90B) with 95% confidence intervals in parentheses. Significant score decreases (p < 0.05) after degradations are highlighted in bold green, implying the evaluation metric can effectively penalize the degradation.
Weaker Robustness than GPT-40 Preprocessing In the manipulation experiments (Section 4.2 GEM with Llama3.2-00 2-99 preprocessing is robust against "GPT4 rephrase" but fails in the "Llama-3. rephrase" and "Meaningless Elongation".
Evaluation Metric
GPTT-40 Rephrase
Llama3.1 Rephrase
Meaningless Elongation
GEM
-0.132
0.124
0. 0.138 (0.086,0. (189)
(Llama Preproccessing)
(-0. (0.1000))))88)
(0.075,0.177
GEM-S
-0.126
0.156
0.112
(Llama Preproccessing)
(-0.173,-0.079)
(0.109,0.203)
(0.061,0.163)
Table 9: Standardized Mean Differences (SMD) d:= (p' 11) pooted
o of scores after manipulations of GEM and GEM-S metrics (preprocessing with Llama-3.2 90B) with 95% confidence intervals in parentheses. Significant score increases (p < 0.05) after manipulations are highlighted in bold red, implying the evaluation metrics are not robust against the manipulation.
Consistent GRE-bench Results Based on the GEM metric with Ilama3.2 -2-9bb preprocessing, we re-compute the GRE-bench scores with ICLR 2023 dataset (Section 5). The Spearman's correlation coefficient between the GRE-bench scores based on the GEM metric with Llama3.2- 3--90B preprocessing and the scores with GPT-4o preprocessing shown in Table is 0.89. The analogous coefficient for the GRE-bench scores based on GEM-S (abstract) and GEM-S (ASSW) are 0.85 and 0.83 respectively. All detailed numerical results are provided in Table 10
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Model Name
Para. Size GRE-hench (in Billion)
GEM
GEM-S(abs.)
GEM-S(ASSW)
openai/gtt-4o-mini 20240718)
8
45.76
19.58
18.27
openai/gpt-4o (v 20240513)
200
50.17
23.12
20.55
opcnai/gpt-3.5-turbo (v 20240125)
~ 20
48.56
20.92
19.04
anthropic/claude-2
137
$4.61
26.07
23.93
anthropickclaude--SHaikuu (v 20240307)
~ 20
52.70
24.87
22.18
anthropiciclaude----onneet (v 20240229)
~
54.83
25.82
22.78
anthropicdclande-3-ppuss (v 20240229)
2000
62.08
33.13
30.53
google/geminnipno 002)
~ 20
46.75
21.33
20.43
google/gemini-pn--155 (v 002)
175
56.36
27.40
24.44
metcllaanalllaan-3----inntttn
47.36
20.96
19.86
matel/lama/lllaa--3-70-----m--
70
55.62
29.61
27.98
misrrlai/mixxxra--kk--inisuuuu (v 0.1)
56
50.53
22.83
21.08
mistrala//mixxtral-k222----ssss (v 0.1)
176
51.57
23.84
21.79
mede-llamn/lllmaa331-------nnut
8
$1.04
24.51
22.28
metelllama/llama33-----------m--
70 50.26
23.68
21.73
mistralai/miistral-aagge (v 2407)
123
51.81
23.59
21.20
mistralai/miittral-small (v 2409)
22
50.22
22.99
20.58
mistralai/mmistral-tnnn mistral-7b-0.3)
7
48.42
20.04
18.86
Table 10: Results GRE-bench based on GEM, GEM-S (abstract), GEM-S (ASSW) with Llama3.2 90B preprocessing. In the column of parameter size, the symbol ~ indicates that this size estimated.
Overall, as more advanced models can preprocess reports more accurately, the GEM metric will be more accurate and manipulation-resiitaua with superior preprocessing. Considering the frequent release and update of L.Mss, utilizing more advanced I.IMS for preprocessing enhances both the accuracy and robustness of the GEM metric. Consequently, our GEM metrics stand to benefit from the continuous advancements in LLM technology.
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A.4 VALIDATION ON YELP RESTAURANT REVIEW DATASET
To further validate our GEM metrics' effectiveness in evaluating judgments without gold-standard references in scenarios in addition to peer review. We use an open-access online business review data from Yelp, and repeat the same workflow in Section 4.2 and 4.3 to check how sensitive GEM metries are to degradations and how robust GEM metries are to manipulations.
The results are similar to those observed in experiments on ICLR dataset (Section 4.2 and 4.3 Our GEM and GEM-S demonstrates significant sensitivity to degradations and robustness against manipulations.
Yelp Review We randomly sample 300 restaurants from the entire dataset which have at least 50 reviews with at least 1000 characters. For each restaurant, we randomly sample 3 customer reviews among all reviews with at least 1000 characters. Yelp reviews quality varies much more than ICLR reviews, thus, we use length as a heuristic filter to get reasonable quality reviews.
GEM Setup We use GPT-4o for text preprocessing, and use Llama-3,1 8B as the evaluation-LM, For GEM-S, the categories of the restaurant the synopsis, which is given by the Yelp dataset.
GEM demonstrates sensitivity to degradations. We reuse the Sentence Deletion and Deletion Completion degradation methods described in Section 4.2 However, since restaurant reviews lack "abstract", the Abstract-only Review degradation cannot be applied. Instead, propose Random Replacement degradation, where we degrade the original review z; by replacing with a random review xf of another randomly selected restaurant.
Table 11 presents the standardized mean differences (SMD) with 95% confidence intervals for GEM and baseline metrics before and after these degradations. The results align with those observed in the ICLR dataset (Section[422 GEM and GEM-S consistently exhibit significant SMD across all three degradations. Additionally, while LMExaminer is more sensitive to degradations like Sentence Dele- tion and Deletion & Completion, which impair presentation quality, GEM shows greater sensitivity the Random Replacement degradation, which severely compromises semantic informativeness.
Evaluation Metric
Sentence Deletion
Delction & Completion
Random Replacement
BLEU
-0.348
-0.113
-0,231
(-0.436.-0.260)
(-0.200,-0.026,
(-0.355.-0.1077
ROUGE-L
-0.677
0.009
-0.345
(-0.765, (6.599))
(-0.064.0.081)
(-0.462,-0.229)
BERTscore
-0.318
0.01
-0.722
(-0.359,-0. -0.276)
(-0.032,0.055
(-0.836,-0.m
BARTscore-Fi
0.744
0.457
-0.042
(0.705,0.77 70774)784)
(0.427.0.44
(-0.163.0.079)
MARTsscre-precistte
0,877
0.536
-0.023
(0.831.0.924
(0.501.03) 00551)
(-0.159.0. 11.90..113)
scoeeccre-ell
-0,015
-0,014
-0.046
6.0.018-0..01
(-0.017, 10.011))
(-0.051.-0.0411)
LMExaminer
-1,245
-0.466
-0.204
(-1.328,-1.162)
(-0.550,-0.382)
(-0.340,-0.06))
GEM
-0,386
-0.238
-0,516
(-0.459.-0.3144
(-0.318,-0.155
(-0.624,-0.408)
GEM-S
-0.361
-0.195
-0.283
(-0.436.-0.287)
(-0.277,-0.114)
(-0.385.-0.182)
Table 11: Standardized Mean Differences (SMD) d:= (1' - pu)) scores after degradations of
various evaluation metrics with 95% confidence intervals in parentheses. Significant score decreases (p < 0.05) after degradations are highlighted in bold green, implying the evaluation metric can effectively penalize the degradation.
GEM demonstrates robustness to manipulation. Similarly, we reuse the GPT-4o Rephrase and 1.. Rephrase manipulations to test whether metric is robust against specific language styles. However, since Yelp restaurant reviews lack a structured format like ICLR peer reviews (as illustrated in Figure 3. it is unclear how to construct a suitable Meaningless Elongation manipulation.
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Table 12 shows the standardized mean differences (SMD) with 95% confidence intervals for GEM and baseline metrics before and after these manipulations. The results remain similar to those observed in Section 4 GEM metrics remain robust against both types of manipulation, while LMExaminer shows a significant increase in scores after LLM rephrasing.
Evaluation Metric
GPT-4o Rephrase
Llama3.1 Rephrase
BLEU
0.100
0,046 (-0.063,0.155
(0.014.0.185)
ROUGE-L
0.020
0.083
(-0.037.0.076)
(0.008 0. 0.1)
BERTscore
-0.016
-0,098
(-0.041.0.0 (,0008)
(-0.133.-0.064)
BARTscore-F1
0.316
0,425
2..00.334
(0.371.0.478)
BART score-precision
0,371
0.508
(0.337,4 405)
(0.445, 0,571)
BARTsccor-reeall
-0.012
-0.016
(-0.013.-0.010)
(-0.018.00.014)
LMExaminer
0.300
0.453
(0.240.0.361)
(0.387.0.518)
GEM
0.021
0,010
(-0.035,0.076
(-0.055,0.075)
GEM-S
0,020
-0.012
(-0.037.0.077)
(-0.080.0.055)
Table Standardized Mean Differences (SMD) of scores d:= o of various evaluation metrics with 95% confidence intervals in parentheses. Significant score increases 0.05) after manipulations are highlighted bold red, implying the evaluation metrics are not robust against the manipulation.
paoled after manipulations
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A.5 EXPLORATION OF PROPERTIES LLM ESTIMATED DISTRIBUTION
An interesting question raised by our anonymous reviewer is:
As we know Pr(x.yj =Pr(a] Pr(y]z] = Pr(y] Pr(slly) does this symmetry also applies to the L.M estimated probabilit,, Le., PTLLM [2) ( = PLLLM PPLLLMML)[[)
To answer this question, corresponding reviews in total. For each pair of reviews 2, for the same pa- per, we use our method compute log PLLLM v). log PrLLM[y/x]] log [aL and log PrL For those 5328 pairs, we compute the Spearman correlation co- PLLLM[xly] == exp (logPELLM(22)) +oggPLLM (B]) and
randomly select 500 papers from ICLR 2023 and 1874
efficient between PLLLM[[] (logPrLLM [x]]+ Log.PLL [xl). The Spearman correlation PLLLM[3] PLLLM[VVa [ == exp
coefficient is 0.943, which indicates that they are highly positively correlated, despite minor differences. Potential noise in the estimation process may account for these differences.
The results revealed high positive correlation between these two values, although they were not precisely equal. Despite this difference, the empirical results in our paper indicate that this does not negatively impact the desired properties, including the accuracy and manipulation resistance.
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B OMITTED PROOFS
Proposition 3.1. When the KL-divergence between the LLM estimated distribution and the underlying distribution satisfies
DEL Pr (r= LLLM
<e, VTи-
For the two candidates H and L discussed above, the information structure of H Blackwell dominates s, when the size of dataset n goes to infinity, we have [(Xx:))2[(xx,>>)
Proof of Proposition 3.1 By the definition of estimated PMI, PMI(x;y) log PELLL = x X=
u]-logPrLLAN yI, we have
T(X:Y) (MI(A.))
log Pr Y= x=
log Pr y = ysl LLM
71
LLM
log Pr Y = y/4 X X=x;]
log Pr =y)
7 LLM
72
LLM
Note that 1 E log PILLM(Y yı] does not change whatever the candidate response follows Xи or XL. In addition, recall that we assume that all task-response tuples (w,, are i.i.d. sampled from the underlying distribution D. Therefore, with the law of large numbers, have ((Xx:)) further equivalent
log Pr YY = y X 2H] >E log Pr Y = X 1=xx1 LLM
LLM
On the left hand side, have
log Pr = = X =zH] L.M
Pr[XH =HH > Pr(Y = X1 X loog
Pr
[Y y|X I.H]]
LLM
Pr[X =n
Σ D X = x==
(This step follows the condition of bounded KL-divergence in Proposition 3.1
Σ Pr[X - Y Y w/]ogPr|Y = yy X TH
#H
H(Y (XH)
On the right hand side, we have
log Pr YY= x =
LLM
-II Σ Pr = Xx =00 Pr Y = y/ X = xn]
LLM
>> =IL) 2 = x X =2
(This step follows the fact that logarithm is a proper scoring rule (Hendrickson Buchler 1971).
=-H(Y|XL)
=[(;;x))-H(Y)
Thee -ivveieeeee between two distributions over the same probability space is Dz. (PI(Q) Σ, ((e))))()p)))))((())
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Since Xи follows an information structure σы Blackwell dominates or that X₂ follows, by the information process inequality, we have I(Y; ((;;L))
Therefore, combining all these together, have
E log Pr x B log Pr Y- x LLM
LLM
and consequently, we have T( 2 T(XL;Y)-C.
Proposition B.1. For given random variable Z, when the Kl--divergence between the L.M estimated distribution and the underlying distribution satisfies
DEL LLM - PX
<e, VEH
For the two candidates H and L discussed above, and the information structure of H Blackwell dominates L's, L's, when the size of dataset n goes to infinity, we have
:(X (2) |
Proof. By following similar steps of the proof of Proposition 3.1 we reduce the problem to showing
E log Pr YY yy X=
=x Z y 2] 2 log Y = X x=x1,
"LMM"
"LM
Again, following similar steps, we have that the left hand side follows
E log Pr Y X
Pr 1 = X1 -== H(Y 2 Z=2)-
=I(YXX (Y (2) 1
Similarly, we have the right hand side
EE
log Pr IY - X x1, =1] LLM
Pr(z==I(1(() X -
x (z)
Therefore, combining all these together, we have
T(X /) > T(XiiV 2) -6
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C IMPLEMENTATION DETAILS
In this section, we provide a detailed interpretation of how we implement our proposed metrics facilitate the replication of our results. This includes comprehensive descriptions all prompts utilized for generating peer-review judgments, preprocessing the judgments, predicting the judgments, as well as other details that help reproduce the implementation. In all LLM API calls our experiments, for reproducibility, we keep the temperature at and the maximum (output) token count at 4000, unless otherwise specified.
C.I JUDGMENTS PREPROCESSING
To filter out the shorteuts in the review judgments, an LLM to rewrite the original review judgments in a certain compressed format, eliminating the impact of various aspects such semantics, syntax, and language styles. Here, employ the LLM GPT-4o (specifically, 200--0-2024-0--111 to preprocess the judgments generated by all candidate LLMs (and human reviewers). Here is the prompt use:
a preprocessing procedure. We employ
System Prompt
Carefully read the text of scientific paper review. You should summarize each evaluation in the review in sepurate line. Begin following phrases: "The reviewer appreciales'. reviewer summary concise possible, excluding specific details about the paper's content,
summury with
reviewer questions'
"The jeviewer suggests'. You need to such as topics, ideas, methods, findings, and any mathematical symbols.
that if multiple evaluations mentioned the split it into separate lines. For example, you should not output a line like 'The reviewer appreciates the well-written paper and experimental performance' contrast, you should output "The reviewer appreciates well-written paper' and 'The reviewer appreciates good experimental performance" in two lines.
You should
original review, you should still
User Prompt
[original Review Judgments
C.2 JUDGMENTS PREDICTION
We compute our metric, the Generative Estimator for Mutual Information (GEM), by utilizing both the original and fine-tuned versions Llama-3. 1 1-8B-Instruc to predict the conditional probability PILMM =y X= =z] and the marginal probability PI (Y = 3]. In this context, and represent preprocessed review judgments. The same methodology is employed for calculating the GEM-S score. Below is the prompt used for prediction.
System Prompt
You are sccond reviower for scientific paper. You are given the abstract of the paper and list review judgments from the lirst reviewer, starting with "The reviewer upprecaatesectiiizzssqqeeet hww.:iiicciussuuuuusoos/nggttt /suggesssssss Your tusk provide your judgments the paper hased the given materials. You should create separate line each judgment you have, starting with "The reviewer Ensure your judgments concise, excluding specific details about the paper's
User Prompt
[Abstract the paper]
(Abstract of the Psper the metric is GEM S, and "Not Available" if the metric GEM)
[Review judgments from the first reviewer]
[Preproceased Review Judgmentia (ar) for conditional probability, and "Not Available" for marginal probability)
Forced LLM Output
[Prepracessad Review Judgmentia (y)
We employ 4-bit quantization.
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C.3 PEER-REVIEW JUDGMENTS GENERATION GRE-BENCH
We provide the implementation details GRE-bench where we employ academic peer review as the task to evaluate LLMs' ability to generate informative judgments. Following Liang al. (222))ss 's research about -Lgeeerrrtteed peer review, we use the Scienceheam pdf parser to convert the ICLR submissions in pdf format text format, and removed the references and all appendices. When using the sciencebeam PDF parser, it retains all image and table titles and captions while disregarding other content. While few ICLR submissions require OCR before parsing into text (5 of all submissions in 2023), none of the 300 papers we experimented with required OCR, ensuring no OCR errors. Additionally, we have verified that the parsed text of papers in our dataset does not exceed the input token limit of the LLM.
While we acknowledge that PDF parsing is an inherently imperfect process that may occasionally introduce artifacts or miss certain elements, our experimental design mitigates potential biases because all LLM models in our evaluation receive identical parsed versions of each paper, and any parsing inconsistencies would affect all models equally, making the relative performance comparisons robust to such noise.
Below is the prompt utilized to request each candidate L.M to produce its review judgments.
System Prompt
are given paper submission top-tier read the poper carefully. Once you have finished reading, please provide the following your review:
Learning conference which you
detailed
First, write concise summary
key points and contributions the paper inside csummary> tags.
Next, think critically about the strengths and weaknesses of the submission. Inside <streagths> tags, numbered list of least 4 key reusons why this paper should potemtially be accepted to the conference. For each reason, use sub bub-bullet points to provide detailed arguments and evidence from the paper support that reason.
Then, inside cwesknesses> tags, give a numhered list of least 4 key reasons why this paper should potentially rejected from the conference, Again, for each reason, use sub-bullet points provide detailed arguments and evidence from the paper support that reason.
After weighing reasons <questions> tags.
against, think some open questions you
List your questions
Remember, reviewer your to rigorously evaluate strengths weaknesses
provide
constructive feedback to the authors. Be thorough, specific and detailed in argumennts and feedback. Highlight both the positives and nogatives you see, and justily your points caretully with reference to the content of the paper.
User Prompt
[Full paper in Text Format)
C.4 SUMMARY OF NUTHOR-STATER STRENGTHS AND WEAKNESSES
In GEM-S (ASSW), we need to embed the strength and weakness text stated by the authors into the input of L.M.. To do this, employ gpt-4o to summarize it using the following prompt.
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System Prompt
You are given paper submission for a top-tier Machine Learning conference. Your goal is to identify and list the strengths and that paper
itself. This requires careful reading paper.
Pleus follow these sleps to complete the task:
puper submission. you identify instances where authors strengths or posilvee aspects of their research, methodology, resalts, or contributions. These are the strengths claimed by the paper. Also, identify instances where authors mention limitations, weaknesses, arcas future improvement their work. These are weaknesses claimed the paper.
Carefully
2. Compile your findings into two separate lists: one for strengths and one for weaknesses.
For each list, write each
separate line, keeping concise. Add extra blank line between each clarity.
4. Format your ouiput as follows:
|strengths claimed by the papery
[List each strength claimed by the paper in separate lines, with an extra blank line between each point]
//ttengths claimed by the papery
jweaknesses claimed by the paper;
[List each weakness claimed by the puper sepurate lines, with extra blank line between each point]
//weaknessess clwimed. by _iheepaper
Important: Frcus only the strengths and weaknesses that the paper claims about itself. Do include evaluation or opinion of the paper's merits or shortcomings. Do not include the strengths and weaknesses of the baseline. Your task is to roport what authors themselves stated werk's strengths and limitations.
User Prompt
[Full papez in Format)
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D EXPERIMENTAL DETAILS
In this section, we provide detailed information regarding the experiments we conducted. This includes the specific processes used to degrade and manipulate reviews, as well as the implementation of the baseline metries.
D.1 BASELINE METRICS
In the paper, we use BLEU, ROUGE-L, BERTScore and LMExaminer as the baseline metrics. BLEU and ROUGE-L are both detail free. We employ NLTK as the tokenizer and utilized existing library functions to compute the BLEU and ROUGE-L scores.
BERTScore (Zhang et al., 2019) evaluates generated text by measuring the cosine similarity between token embeddings from pre- rre-trained language model. Instead of BERT, we use recent model as the evaluation-I.M for better token embedding performance.
stella en 400M v5 (Zhang 2024)
BARTScore (Yuan et al., 2021) is a probability -based metrie. It has three variants, precision, recall, and F1-score. Instead of BART, we use the state-of-the the-art Llama3. 1-8b as the evaluation-LMM for estimating the probability, which the same as our GEM metrics.
I.EExamner (Bai al. 2024 is one of the recently popular methods where language model (LM) used evaluate the quality generated text based on specific evaluation criteria, serving as an examiner. With empirical evidence suggesting that GPT-4 outperforms open-source models and even finetuned models pointwise grading on AlignBench (Ke et al.) 2023), we use GPT-4o as baseline in experiment. For peer review tasks, our prompt adopts criteria based on Review Quality Indicators (RQIs) (Goldberg et al. 2023 Van Rooyen et al.] 1999 SSuperchi et al.) 2019) including four aspects, understanding, coverage, substantiation, constructiveness.
We input the full text of the paper along with the review to be judged into the LLM and prompt score based on RQIs. We show our prompts below.
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System Prompt
You are an expert tasked with ovaluating the quality of a review for a Machine Learning paper. Your goal is to aseess how well review critiques the paper provides valuable feedback authors. The paper will given after "Paper]' will he given after [Review]'.
judge quality of this consider following
1. Understanding: Does the reviewer demonstrate clear understanding of the paper's main contributions, methodology, and results?
Coverage: Does review results, and conclusions?
major aspocts the paper, including the problem statement, methodology, experiments,
3. Substantiation: Does the reviewer provide specitic examples or references from the paper support their comments and eriticisms?
4. Constractiveness: Does the review offer helpful suggestions for improvement or identify areas where the paper could be strengthened?
For each criserion, provide detailed analysis of how well the review meets standard.
Afler unalyzing each criterion, provide an overall ussesmmnt the review's quality. Consider how well it serves its purpose offering valuable feadhack to the authoes.
Finally, assign score the review on a scale of to 10, where the lowest quality, 5 is the average quality, and 10 the highest quality.
Present your evaluation in the following format:
canalysis>
[Your detailed analysis bere]
</analysis>
>
[Your overall ussessment here]
<loverall assessment>
coverall.score>
[Your final quality score here]
<Joverall score>
User Prompt
[Paper]
[Full paper in Text Format)
[Review]
[original Raview Judgmentss)
D.2 VALIDATION WORKFLOW FOR DEGRADATIONS AND MANIPULATIONS
ALGORITHM 1: Validation Workflow
Input: A dataset /) with n tuples of tasks and associated text responses.
An evaluation metric / computing the scores. A degradation/mannipulaion strategy M.
Output: Statistics metrics.
for i= to n do
Get the i-th tuple from the dataset: task wy, candidate response I, and reference response y: : Compute s; = (.,):
Replace the response x; with æ according to degradation/mmanipulatoo strategy M; Compute < (w,,aa())))
end
Compute the means napx and standard deviations a.a' of (siJaeja) and (5/het respectively.
28

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Published as a conference paper at ICLR 2025
D.3 DEGRADATIONS
D.3.1 REVIEW DEGRADATION: SENTENCE DELETION
The ICLR 2023 review comments must follow a specific formatting with four sections including "Summary Of The Paper", "Strengths And Weaknesses". "Clarity, Quality, Novelty And Repro- ducibility" and "Summary Of The Review". When performing sentence deletion, we maintain the original format unchanged. Other sentences exclamation points, and line breaks, which mark the end of a sentence. In each section, we retain all sentences with odd-numbered positions and delete those in even-numbered positions. Below is toy example for illustration.
identified based periods, question marks,
Before Deletion
Summary Of Paper:
This first sentence. This second sentence. This the third sentence.
Strengths And Wenknesses:
This is the first sentence. This is the second sentence. This the third sentence. This the fourth sentence.
Clarity, Quality, Novelty And Reproducibility:
Summary Of The Review:
This is the first sentence. This is the secund sentence.
After Deletion
Summary Of The Paper:
Strengths And Wenknesses:
This is the first sentence. This is the third sentence.
Clarity, Quality, Novelty And Reproducibdity:
Summary Of Review: This is the first sentence.
D.3.2 REVIEW DEGRADATION: DELETION & COMPLETION
When applying the deletion & completion method degrade reviews, initially delete half of the sentences, similar to the sentence deletion process, and replace the omitted content with "[There one missing sentence]". Subsequently, utilize GPT-40 complete these sentences. Below is a toy example.
Before Deletion
Summary Paper:
first sentence.
second sentence.
sentence.
Strengths And Weaknesses:
This is the first sentence. This is the second sentence. This the third sentence. This is the fourth sentence.
Clarity, Quality, Novelty And Reproducibilit::
Summary Of The Review:
This is the first sentence. This is the sccond sentence.
After Deletion
Summary Of The Paper:
first sentence. [There one missing sentence]
sentence
Strengths And Weaknesses:
This is the first sentence. [There is one missing sentence] This is the third sentence. [TTeere is one missing sentence]
Clarity, Quality, Novelty And Reproducibilit::
This first
Summary Of Review:
This is the first sentence. [There is one missing sentence]
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Published as a conference paper at ICLR 2025
Hore, present the prompt we utilize to leverage GPT-4o for completing the deleted sentences.
System Prompt
You are tasked with completing missing sentences of peer review evaluation given by the user while keeping all its existing sentenees. Your goal complete missing sontenees indicared by [Ther one missing sentence] of this peer review evaluation, following these rules:
Insert new sentences that don't I contribute signiticunt information.
Place these seniences belween existing
where they most natural.
Keep the avenall language style similar.
Ensure these added sentences don't 't contradiet substantially alter the original content.
Remember to maintain essence and under original content while completing all missing sentences indicated by [TTere missing sentence).
User Prompt
(Judgmentia after Delet neletion)
D.3.3 REVIEW DEGRADATION: ABSTRACT-ONLY REVIEW
In this degradation process, utilize Bruude---soneee to generate review based solely the paper's abstract, which serves as a substitute for the original review. Below is the prompt we use complete this task.
System Prompt
You given abstract paper sahmission for top-tier Machine Learning conference. need write detailed review of the paper with only the abstract. Please read the abstract carefully, Once you have finished reading, please provide the following your review:
First, write concise summary of the key points and contributions of the paper in "Summary Of The Paper" section.
"Strength And Weaknesses" section, critically
strengths weaknesses
submission.
numhered list of at least 4 key reasons why this paper should potentially he accepted to the conference. For each reason, use sub-bullet points provide detailed arguments
evidence from the paper to support reason. Then, give numbered of at least key reasons why this paper should potentially be rejected from the confereace. Again, for each reason, use sub-bullet points to provide detailed arguments and evidence from the paper to support that reason. After weighing the reasons for and against, dhink questions you
the work.
Then, finish the "Clarity, Quality, Novelty And Reproduciillity and "Summary Of The Review" sections.
rigorously evaluate the strengths weaknesses of the work and provide critical but constructive foedback to the authors. Be thorough, specific and detailed in your arguments and foodback. Highlight both the positives negalives you see, and justily your points carefully reference the
Remember, reviewer your
paper.
User Prompt
(Abstract #sper]
D.4 MANIPULATIONS
D.4.1 REVIEW MANIPULATION: LLM REPHRASING
To rephrase the review without altering the semantics, employ GPT-4o and Llama-3.1 1-instruct to rewrite the original review judgments, modifying the phrasing while maintaining the original meaning. The same prompt is used for both models accomplish this task.
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Published as a conference paper at ICLR 2025
System Prompt
You are tasked with rowriting a peer review evaluation. You should follow these guidelines:
Maintain overall
organization reviow.
2. Improve the writing and make the language more natural and native-soudingg.
any grammatical
phrasing.
Remember to maintain the overall structure and content of the original review, but aim to cuhance its readability and fluancy.
User Prompt
{Original Review Judgments) s}
D.4.2 REVIEW MANIPULATION: MEANINGLESS ELONGATION
We employ verbose and uninformative template content alongside summaries of the existing review achieve a meaningless elongation of the original review judgments. Specifically, adhere to the fixed review format established by ICLR 2023, which consists of four sections. At the beginning of each content section within these sections, we incorporate an identical introductory statement. Additionally, we utilize GPT-40 to generate one-sentence summary for the "Strengths and Weaknesses" section of the review, embedding this summary within the added text. Below example, including the meaningless content add to the original review.
Before Manipulation
Summary Of The Paper:
This is the first sentence. This is the sccond sentence. This the third sentenoe.
Strengths And Weaknesses:
This the third This the fourth Clarity, Quality, Novelty Reproducibility:
This is the first sentence.
Summary Of The Review:
This first sentence. This second sentence.
After Manipulation
Summary Of The Paper:
This section provides an overview of the key contributions, methodologies, and findings presented in the paper. It summarizes main urguments and highlights scope signilicance the research conducted. Specilically. the first This is the second sentence. This the third sentence.
Strengths And Wenknesses:
paper, focusing on contrbutions field. methodological rigor, and areas where improvments could be made. The aim provide constructive feedbuck that can help enhance the quality of the work. This paper has several notable streagths, including (Strength Summaryl J. However, there are some areas that could improved, such {Neakness Summary). ). Specifically, discuss the strengths and weaknesses the puper following. This second sentence. This the third sentence. This the fourth sentence.
section,
strengths and
This first
Clarity, Quality, Novelty And Reproducibility
I will evaluate the clarity the paper's ss presentation, the quality of the research, the novelty of the findings, and the reproducibility assessment address whether insights, and can be replicated bused on the provided information. This is the first senience.
experiments.
presented clear coberent manner, offers
Summary Of The Review:
summary encapsulates the main points the reflecting the paper's contributions, strengths, for improvemett It provides balanced overview of the paper's impact and offers final recommenaaiios for the authoes. This is the sentence. This the second sentence.
The prompts that request GPT-40 to generate the Strength Summary and Weakness Summary are presented as follows.
System Prompt
You given review of scientific paper, identify key ("strengths" "weaknesses") of the scientific paper from the roview in a single concise line, using two phrases separated by 'and".
User Prompt
[Original Review Judgments)
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Published as a conference paper at ICLR 2025
E GEM GEM-S WITH FINE-TUNED EVALUATION-LM
We not only use the original Llama-3.1 3.1-888... 1-8B-Instruct as the Evaluation-LMM, but also fine-tune to to better estimate the conditional and marginal probabilities. In this section, provide the details of how we fine-tune the model, as well as the experimental results.
E.1 IMPLEMENTATION
We fine-tune the original Llama-3. 3. 1-8B-Instruct model using 1,000 papers and their corresponding reviews from ICLR 2023 (which are separate from the papers used for experiments). Let gen(metrie,xx.p represent the function that generates the full prompt for predicting judgments (both input and output), where I -Nll indicates that we are estimating the marginal probability. For each paper, we randomly selected three reviews, denoted as r1, 72, and 73, along with their preprocessed versions f1. f2, and f3. From each paper, we generated nine data points for GEM and nine data points for GEM-S as follows: gen(GEM (-S) f1. f2), gen (-S) -), f1, gen(GEM (-S) f2) gen (-S) ff2, gen(GEM (-S) ff),f1), (GEM (-S) ffa,f2),
gen (-S) ,Null ), gen (-S) ,Nul f22) (GEM (-5) Null,fa).
In total, this process yield 18,000 input-output pairs. We used the Unsloth framework to fine-tune the model, running on a physical server equipped with single NVIDIA A100 GPU, provided by Google Colab. Here is the settings use for fine-tuning:
"nodel "sodel_confi"":
"base_model" hww.s//ttt/tttttllalaa...a 3.1-3111111111111 cessstrcct-bbbdaaee The base model men_soo__engthh 4096, The maximum sequence langth "load_in_4bil True, Toad the model ir 4-bit
ora cra_config";
"y"; 16, The number of LoRA layers 8, 16, 32, 64 "1ora_s The sipha value for LoRA "lora_dropou=":0, # The dropout vaice for LORA
"traiiingg_ontii
"gec_ deweviee__rannaaateeeee
2,
batch
4, The gradient acoumulation stepa Saammupsstepa" 5, The warmup steps
stax_steps":,, # The maximum steps "num tham_train_spoche 3, The number of training spochs "learning_razee 2e-4, "optim" "Madamm_Bbit", J The optimizer "weight_decay" 0.01, The weight decay "Ir_scheduler_tyy "linear", The learning rate scheduler "seed" : 42, The seed
rg vate
E.2
EXPERIMENTAL RESULTS
Evaluation Metric
Sentence Deletion
Deletion & Completion
Abstract-only Review
GEM-raw
-0.123
-0.126
0.020
(-0.126.-0..2) 120)
10.132.-0.121.
(0.017.0.023)
GEM
-0.401
-0.308
-0.191
(-0.448.-0.3 -0.354)
(-0.358,-0.2555
(-0.261,-0.1) -.222))))
GEM-S
-0,409
-0,206
-0.566
(-0.455.-0.362)
(-0.254.-0.15%%
(-0.639,-0.992)
GEM-finctune
-0.155
-0.135
-0.043
10.11990001110
(-0.180,-0..
(-0. 106,0.020)
GEM-S-finetun
-0,192
-0.099
-0.355
(-0.244.-0.140)
100.148--00049)
(-0.422.-0.288)
Table 13:
Standardized Mean Differences (SMD) d:= (f'- ((''=))/or p pooked of scores after degradations of various evaluation metrics with 95% confidence intervals in parentheses. Significant score decreases (p < 0.05) after degradations are highlighted in bold green, implying the evaluation metric can effectively penalize the degradation.
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Published as a conference paper at ICLR 2025
Evaluation Metric
GPT-4o Rephrase
Llama3.1 Rephrase
Meaningless Elongation
GEM-raw
-0.123
-0.126
0.020
(-0.126.-0. 10))
(+0.132,-0.121)
(0.017.0.023)
GEM
-0.058
-0.107
-0.063
(-0.090, -0000)
(0.143,-0.0700
(-0.097.-0.055)
GEM-S
-0.046
-0,114
-0,070
10.079.-000113
(-0.149.-0.078)
(-0.104,-0.036
GEM-finee fiMfffinttne
-0.014
-0.010
-0,056
(-0.044.0.015))
(-0.044.0.024)
(-0.088, -0.025)
GEM-S-finetune
-0.026
-0.045
-0.074
(-0.062,0.010)
(-0.085,-0.004)
(-0.112,-0.00)
Table Standardized Mean Differences (SMD) of scores d: (j²' 11/ p after manipulations of various evaluation metries with 95% confidence intervals in parentheses. Significant score increase (p< 0.05) after manipulations are highlighted in bold red, implying the evaluation metric are not robust against the manipulation.
E.3 GRE-BENCH BENCH BASED ON FINETUNED GEM AND GEM-S
Cluude-J-opus Claude-3-hello
Claude
Gemini-pro-1.5
Gemini-pro-]
Parameter Size
Lama 3.1-70b
10ta
Usme-3.1-88
Ueme-3-700
Mistral-
Mistral-tnny
Mistral 8x22b
10
Wistral-8770
32
28
24
20
15
6 10
12
14
GRE Bench (GEM finetune))
GRE Bench SGGEM---iinetnne
Figure 5: Results of GRE-bench based on finetuned GEM and GEM-S with 90% confidence intervals vs. model parameter size. The grey line represents the average human baseline, with the 90% confidence interval shaded in grey.
33


Reviews:
[{"review_id": "zBLr573tyf", "reviewer": "Reviewer_8Skk", "summary": "The paper introduces GEM, a novel metric for evaluating the performance of large language models in generating informative judgments using given references but not necessary gold standards, and presents GRE-bench, a benchmark for assessing the quality of peer reviews generated by LLMs.\n\nGEM is designed to assess the informativeness of judgments in scenarios where gold standard references are unavailable, expanding the scope of benchmarking to subjective tasks like peer review.", "questions": "Using LLM to estimate the conditional probility seems problematic:  While P(x,y)=P(x)*P(y|x)=P(y)*P(x|y), P_LLM(x)*P_LLM(x|y) != P_LLM(y)*P_LLM(x|y).  I wonder if this will cause problems in the estimation.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Using mutual information for evaluation subjective generative response is novel as my understanding.\n\nThe paper give theoretic guarantees of the proposed method.\n\nExperiments show the method works well on the task of evaluating reviews for academic papers or student proposals.", "weaknesses": "Only one type of evaluation tasks has been assessed: paper review evaluation.  It would be more convicing if the paper can conduct experiments of more subjective evaluation tasks, such as summarization, dialog, etc.  \n\nThe paper uses Llama 3.1 8B as the evaluation LM.  I would like to see how different LMs effect the performance of the evaluation.\n\nFigure 4 is not mentioned in the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "E5YSCyWy2D", "reviewer": "Reviewer_CxHL", "summary": "This paper presents GEM (Generative Estimator for Mutual Information), a new evaluation metric for assessing the quality of text responses generated by LLMs in tasks that lack a clear gold standard reference.  GEM aims to provide an accurate and manipulation-resistant metric by estimating the mutual information between candidate responses and a set of reference responses, which are not required to be perfect. The main idea is to measure the semantic informativeness of candidate responses by filtering out aspects like language style and syntax, focusing on how much information the candidate's response reveals about the references.", "questions": "This is a kind of optional question out of my curiosity. \n\nIt seems that GEM is a relative scoring/evaluation framework. If so, what would be the pros and cons of GEM compared to other relative evaluation frameworks, such as the ELO rating system commonly used in generation task leaderboards, like those in Chatbot arenas?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is clearly written and rigorously formulated. The research topic is very timely and important, given the increasing use of LLMs and the challenges of evaluating their performance in subjective tasks.", "weaknesses": "I have some questions and concerns:\n\n1. Impact of Suboptimal Human References: I am concerned about GEM's reliability when human references are of lower quality than LLM outputs. Could the LLM used for estimating probability distributions learn to favor responses that resemble flawed human references, even if they are less informative, potentially leading to inaccurate scores? \n\n2. Performance stability according to the choice of evaluation-LM and preprocessing LLM: How sensitive is GEM to the choice of LLMs used for evaluation (Llama-3.1) and preprocessing (GPT-4)? Would using different LLMs, especially open-source ones, impact the results? LLMs are released and updated frequently, and I wonder how we can deal with such frequent updates to make the GEM score consistent and reliable over time.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZTT1TA0gHW", "reviewer": "Reviewer_bCjd", "summary": "The paper mainly discusses benchmarking LLM's judgments in the lack of gold standard reference answer, but with a pool of vanilla reference answers by estimating the mutual information between a candidate and reference. The authors also show that this technique is also robust against perturbations and manipulations. Lastly, they also present a benchmark/test set to evaluate how good LLMs can generate high-quality reviews for academic paper.", "questions": "- Missing citations: \n    - [Finding Blind Spots in Evaluator LLMs with Interpretable Checklists](https://arxiv.org/abs/2406.13439)\n    - [Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?](https://aclanthology.org/2024.findings-eacl.71/)\n- In Appendix D3, I see that the whole paper is given in text format, but most of the scientific literature also contains images that are quite important and referenced multiple times. What happens in that case? Similarly, how are tables being managed? Also, are these papers converted from PDF to text format, if so, are they being verified for OCR errors? The papers presented are usually 10 pages in ICLR and 8 pages in *CL, so are you sure the paper is not beyond the token limit of the LLM? Lastly, which LLM was used for the prompt in D3?\n- For ICLR dataset, it is mentioned that one reviewer is the candidate and the remaining 2 are reference. Aren't only 2 references too less? I feel in this case the sample bias will be quite high. I urge the authors to clear this for me. \n- For the rephrase manipulation, were any sanity checks conducted to make sure that the semantics did not change? Since we are operating at very long-context lengths (document-level), this could be an issue for smaller models like llama-3.1-8b. GPT-4o is quite large and powerful as compared to llama-3-8b and has much better instruction following capabilities, so was this disparity maintained purposefully? If not, I feel llama-3.1-405B or any of the other larger models should be used in place of it which are good at instruction-following.\n- For the GRE-Bench, where the LLM is generating reviews, were all 3 human reviews taken as references?\n- Line 472 is very redundant and but obvious. Smaller models do not have good instruction following abilities. \n- The word Table has been misspelled as Tabel at a couple of places. Please fix it. \n- In Figure-4 (and Table-6), I am quite dubious about the parameter counts being mentioned there, as most of closed-source models do not reveal anything, especially for GPT models. Also, the color-scheme in Fig-4 is not comfortable to the eye to make clear demarcations across the bars. Please fix that as well. \n- In Prompt D2, the model is provided with one review, which resembles a 1-shot example in a way. In case of generative tasks, there have been cases when the model tries to follow the verbatim or structure of the provided few shots, and the final result lacks creativity. Was this checked in any way? \n- What were the generation parameters in each prompting case?\n- How were the outputs checked for hallucinations as the generations are quite long?\n- In F1, was the same prompt format as in D2 used while finetuning the model?\n- There should be some comparison of llama-3.1-8b-Instruct and its finetuned version (as mentioned in F1) to see the gains due to finetuning. Also, finetuning an already finetuned model is slightly tricky as the distribution shift may cause the model to collapse. The general trend is to use a base model directly for this case.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The paper is well-written, and the problem statements is well-motivated. The authors provide cogent and coherent arguments throughout the paper. I personally think this is a very valid issue for LLM-based evaluators as not always we have a single human-verified gold-standard reference, but a set of good-enough responses to aggregate and use as a ground-truth. The paper also has a good mathematical rigor to prove its hypothesis. \n- The benchmark created in the dataset is a plus point as it can help future works. \n- I also appreciate the finding that GEM is robust to perturbations as lately there has been a good discussion around LLM having blind spots in evaluation and missing out on subtle disturbances to the input.", "weaknesses": "- I do not see any blaring errors in the paper, however, I am a suspicious about the experimental setup as such. Bottom line, the problem statement discusses a **very challenging** problem: _LLMs generating \"judicious\" reviews for very long and complex scientific papers_. I am not sure if most of the existing LLMs are good enough for such a humongous task and if they do provide good results which can be analyses with some guarantee, as the LLMs are required to have a decent knowledge of the domain as well as a good understanding of the hypothesis in the paper, which even humans struggle to understand in some cases. In a way, this feels like just applying an LLM to a task to see how it fares. But the authors to talk about previous works that have discussed using LLMs as reviewers, and I see that most of them have shown negative results in such cases.\n- Related to the above point, I have several questions for the authors, which I hope will be answered to give me some more clarity on the nitty-gritty details of the work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Uxry605peg", "reviewer": "Reviewer_jK45", "summary": "This work is motivated by the importance of finding a benchmarking method that is not reliant on gold-standard references. The proposed method(s) also attempt to circumvent known issues about using generative models as evaluators, where they are often sensitive to degradation strategies and score-increasing manipulations. The method is empirically compared against a wide range of widely known evaluation metrics to showcase its effectiveness.\n\nThe authors also introduce a new benchmark for review generation which is built upon their method that inherits all its benefits. The benchmark is also claimed to combat data contamination by being updated with new review data as it comes out.", "questions": "1. I'm not sure I understand the setup of the first result, am I right in understanding that what you do here is generate k candidates per review, and then get the average GEM score for each candidate by averaging over all other generated candidates when used as references? And then you make the classification by choosing the candidate that resulted in the highest GEM score? How is the classification made? If I'm correct in understanding this, then I don't see how the following sentence from the conclusion applies: \"...while our method does not require gold-standard references, it requires reference that includes original human-generated information.\" Since the way I understand it now, it doesn't really need any human-generated references to generate these scores.\n\n2. Why do you think LMExaminer performs so much better in Sentence Deletion and Deletion & Completion? I feel like that's quite a significant result that hasn't been discussed.\n\n3. \"Figure 5 shows that, within each LLM family, such as the Llama-3 or Claude series, models with larger parameter sizes tend to perform better. Moreover, newer versions tend to outperform their predecessors with similar model sizes, as seen in comparisons between Llama-3.1 and Llama-3. These observations further validate the effectiveness of our GEM and GEM-S metrics in accurately evaluating LLMs.\"\nCan GRE-Bench only work with the GEM metrics? Do you think you would observe something similar if you used different metrics?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The paper presents a valuable step towards doing away with the reliance on gold-standard references in evaluation. It's original and creative in the way it makes use of mutual information in the context of generative models.\n\nThe method is described rigorously and thoroughly. The flow and structure of the paper makes sense and conveys their ideas well.\n\nThe authors also seem to be highly aware and open about the limitations of the method. The paper actively critiques itself and presents answers and discussions on these critiques.", "weaknesses": "While the paper is generally highly verbose discussion-wise, I find the experiments and results parts of the paper to be a bit lacking in discussion and analysis. My concerns on this matter are made more specific in the \"Questions\" part of this review.\n\nSome minor grammar/spelling mistakes: lines 96-97 \"..the score can relies solely...\", line 455 \"Table\" is misspelled.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)