CriticEval: Evaluating Large-scale Language Model as Critic
Authors: Tian Lan
Wenwei Zhang
Chen Xu
Heyan Huang
Dahua Lin
Kai Chen
Xian-Ling Mao
Venue: NeurIPS 2024 poster (2024)
Keywords: Critique ability
LLM
evaluation
Primary area: natural_language_processing
TL;DR: A comprehensive and reliable benchmark for evaluating critique ability of LLMs

Abstract: Critique ability, i.e., the capability of Large Language Models (LLMs) to identify and rectify flaws in responses, is crucial for their applications in self-improvement and scalable oversight. While numerous studies have been proposed to evaluate critique ability of LLMs, their comprehensiveness and reliability are still limited. To overcome this problem, we introduce CriticEval, a novel benchmark designed to comprehensively and reliably evaluate critique ability of LLMs. Specifically, to ensure the comprehensiveness, CriticEval evaluates critique ability from four dimensions across nine diverse task scenarios. It evaluates both scalar-valued and textual critiques, targeting responses of varying quality. To ensure the reliability, a large number of critiques are annotated to serve as references, enabling GPT-4 to evaluate textual critiques reliably. Extensive evaluations of open-source and closed-source LLMs first validate the reliability of evaluation in CriticEval. Then, experimental results demonstrate the promising potential of open-source LLMs, the effectiveness of critique datasets and several intriguing relationships between the critique ability and some critical factors, including task types, response qualities and critique dimensions.

CRITICEVAL: Evaluate Large Language Model as Critic
Tian Lan'+
Wenwei Zhang Chen Xu*
Heyan Huang'
Dahua 2in Kai Chen Xian-Ling Mao
School of Computer Science and Technology, Beijing Institute of Technology 2 Shanghai AI Laboratory *MMLab, The Chinese University Hong Kong Keyy Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education, Beijing Institute of Technology CCII under InnoHK https: //pps:/ wwwpeittu...ooopppoeeoooeeo conn sonpeennccmmsss/iiiiiivlllll
Abstract
Critique ability, i.e, the capability of Large Language Models (LLMs) to identify and rectify flaws in responses, crucial their applications in self-improvement and scalable oversight. While numerous studies have been proposed to evaluate critique ability of LLMs, their comprehensiveness and reliability are still limited. To overcome this problem, we introduce CRITICEVAL. novel benchmark designed to comprehensively and reliably evaluate critique ability of LLMs. Specifically, to ensure the comprehensiveness, CRITICEVAL evaluates critique ability from four dimensions across nine diverse task scenarios. evaluates both scalar-valued and textual critiques, targeting responses of varying quality. To ensure the reliability, a large number of critiques are annotated to serve as references, enabling GPT-4 evaluate textual critiques reliably. Extensive evaluations of open-source and closed-source I.LMs first validate the reliability of evaluation CRITICEVAL. Then, experimental results demonstrate the promising potential of open-source LLMs, the effectiveness of critique datasets and several intriguing relationships between the critique ability and some critical factors, including task types, response qualities and critique dimensions.
1 Introduction
Critique ability crucial for the self-improvement 2 of LLMs, as it enables the effective analysis and correction of flaws in responses 3.4. This capability also facilitates a more robust framework. i.e. scalable oversight 6) for ensuring the AI systems growing in scale and capability remain aligned with human-desired outcomes and ethical standards.
So far, while numerous works have been proposed evaluate critique ability of LLMs in downstream tasks, like common NLP tasks 7 and reasoning tasks [4]), their comprehensiveness and reliability limited. Specifically, existing works [1 12 typically evaluate only specific aspects of critique ability, resulting in limited evaluated critique dimensions IT [0 [4]. insufficient analysis of response qualities and task types 4 97. Besides, while GPT-4 is frequently used evaluate textual or natural language critiques [3] 10
[3]. its reliability across all critique dimensions and tasks remains unverified [TO [5]. In summary, comprehensive and reliable benchmark for assessing critique
capability of LLMSs is still under-explored, significantly impeding the in-depth analysis.
Equal contributions
Corresponding author
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Table 1: Comparison between the test and dev set of benchmarks and CRITICEVAL. A complete list are described in Appendix The response quality in some benchmarks is unclassified (-). PR denotes the Pass Rate reasoning and coding tasks. Our concurrent works marked with a
Critique
Critique Dimension
Respouse Quality
Test NL Data Size
Subjective Metric
Objective
Human Anno.
Benchmarks
Format
Metric
Release
CRITICBENCH 9
Scalar
2
0
Accuracy
x
X
Shepherd [10]
NL
352
GPT-
X
X
Auto-J [11
NL/Scalar
232
GPT-
Accuracy
X
UltraFeedback
NL
450
GPTT4
X
X
CRITICBENCH
Scalar
2
0
FI.PR
V
MetaCritique R3
NL
300
GPT-4 Ref.
SumuEval 8
Scalar
0
Correlation
WMT-22 (th-en)
Scalar
0
Correlation
MT-Bench
77
Scalar
0
Accuracy
CRITICEVAL (Ours)
NL/Scalar
4 3,608
GPT-4 Ref.
Correlation,
To fill this gap, we propose novel benchmark, CRITICEVAL, designed to comprehensively and reliably measure critique capability of LLMs. Specifically, to ensure comprehensiveness, CRITICE- VAL evaluates critique ability of LLMs from following dimensions: evaluating single response (feedback), comparing pairs of responses (comparison), correcting the response based on feedback (correction) and evaluating one feedback of LLM meeda-feed These critique dimensions cover all categories of critiques in previous works [1]
13 4 and the necessary capabilities for self-improvement of LLM | and scalable oversight 6 These critique dimensions are measured under nine diverse task scenarios, including three common NLP tasks, two alignment tasks, and four math reasoning and coding tasks. Moreover, evaluated responses in each critique dimension and each task are collected using various open-source and closed-source LLMs with different capabilities, with human annotation ensuring varied quality levels. Furthermore, since both the scalar-valued and textual formats of critique are commonly used in these scenarios 3. CRITICEVAL evaluate the critiques in both formats, equipped with objective 18 19] and subjective [ evaluations, respec- tively. Note that scalar-valued critiques typically refer to Likert scores and preference labels, while textual critiques refer to more fine- graingrrind textual analysis about response quality [. Overall, as shown in Table [ CRITICEVAL exhibits significant advantages in comprehensiveness compared to previous benchmarks. demonstrates great diversity in critique formats, critique dimensions, response qualities and the data size of textual critique.
To ensure the reliability of evaluating textual critiques in CRITICEVAL, a large number of high-quality critiques are annotated, serving references for GPT-4 to evaluate textual critiques automatically. To annotate these textual critiques efficiently, we employ numan-in-the-loop pipeline 200,, first generated by GPT-4 and then rigorously reviewed and refined by human experts.
Extensive evaluations of 35 widely used open-source and closed-source LLMs prove the reliability of CRITICEVAL. Specifically, GPT-4 with correlations with human judgments, while removing them results in significant performance loss. Additionally, critiques with higher scores consistently lead to superior improvements. illustrating clear correlation between the real critique ability of LLMS and their evaluation scores within CRITICEVAL. Then, extensive evaluations results also demonstrate that some open-source LLMs, such as Qwen [21] and L2ternLM2 [22]. are approaching state-of-the- closed-source source LLMs in critique capabilities, and their critique ability could be further improved through scaling strategy. Besides, the effectiveness of critique datasets also validated. Finally, these evaluation results also reveal several intriguing phenomena:
reference critiques achieves close
Critique difficulty varies by task type. For instance, math reasoning and coding tasks are more challenging for feedhack, comparison, while they are easier for meta-feedback.
There is an inverse relationship between the quality of critiques and responses. For example, high-quality responses pose a greater challenge to critique effectively.
. Critique difficulty correlates with the critique dimensions; notably, comparison and meta- feedback dimensions present greater challenges than feedback dimension.
These observations and phenomena promote an in-depth understanding of critique ability of LLMs. We hope the discoveries could spur future research in this field.

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2 Related Work
2.1 Application of Critique Ability
Automatic Evaluation Automatic evaluation, also known as critique ability in recent works [3] 10] aims accurately judge the evaluated
has been well studied in the past few years [23 [8]
responses in numerous NLP tasks and reduce the high cost of human annotations [18 17 24 25 26].
Recently, advanced LLMs, like GPT-4, have exhibited very close correlation with human judgments assign textual critiques with corresponding quality scores, i.e., the ccalar-valued score, in
[8] 13] a chain-of-thouuhh inference manner [28] To further mitigate the high inference cost of closed-source LLMs, numerous works propose to improve critique ability of open-source LLMs by fine-tuning
them on critique datasets generated by GPT-4 [4 29 30,
like Auto-J 11 and UltraFeedback [12]
LLM Self-improvement So far, critique ability has been widely used for self-improvement of LLMs in two stages: (1) Inference stage: Given textual critiques that analyze the flaws in the response and provide suggestions, L.LMs can iteratively improve the response quality 66. 13 31 32 33
(2) Training stage: Scalar-valued critiques are frequently used to compile responses with clear performance gap for rejective fine-tuning (RFT) or preference learning (RLHF (44), which further
enhances LLM capabilities 2 5 35
33]. For instance, Self-rewarding M improves Llama-2-70B by fine-tuning it on samples selected based on its rewards. Similarly, ChatGLM-Matt [36] fine-tunes math-critique model for scoring generated answers, which are used for rejective fine-tuning (37
and direct preference optimization [38].
2.2 Benchmarking Critique Ability
So far, numerous meta-evaluation benchmarks have been proposed to evaluate the critique ability of models [18]. Early benchmarks mainly focus on evaluating scalar-valued critiques [30 24] on common NLP tasks [18, 23), like translation 77 and summary [8] by computing the correlations between model and human judgments. Recent works also assess scalar-valued critiques of LLMs on reasoning and coding tasks 9]. For example, CRITICBENCH [ built from 3 reasoning tasks, analyzes important properties of critique ability of LLMs. Our concurrent work, CRITICBENCH [4] analyzes several intriguing findings among generation, critique and correction capability responses collect from five reasoning tasks] Compared with these existing works, our proposed CRITICEVAL exhibits advantages several crucial factors, like critique dimensions, response qualities and diverse task types, leading to more comprehensive evaluations for critique ability. Although CRITICBENCH 9 collect high-quality responses, their quality levels still unclassified.
Beyond scalar-valued critiques, evaluating textual critiques is more challenging [0. 15 Most existing works coarsely evaluated textual critiques using GPT-4 [I] [22, proven unreliable 10 13
Unlike them, our extensive results prove that GPT-4 with -ananttne critiques is reliable for evaluating textual critiques. Although our concurrent work, MetaCritique [13), demonstrates the reliability of evaluating textual critiques by verifying their Atomic Information Units, it is unclear whether their conclusions could be extended to more critique dimensions and tasks.
3 Preliminaries
We first formally define the key concepts and their corresponding notions in CRITICEVAL. Figure shows a specific case understand these concepts.
Task Input (!) and Response (R) represent the queries and generations of LLMs, respectively.
Critique aims to analyze and refine the generated responses. Formally, this paper studies the critique capabilities in four dimensions: (1) feedback F, involves textual analysis and quality score. Good feedback should not only find flaws in responses but also provide helpful suggestions for correction [6) (2) correction or refinement CR aims to revise responses with without feedback. Previous evaluations 12 9 overlook this dimension, although is an inevitable step when letting the model improve itself [35;; (3) comparison F. contains textual critique and a preference label for a pair of responses (Ra, Re); (4) Meta-feedback (F(F)). ). i.e., the feedback of feedback itself [6]. involves a rating score reflecting the quality of F, and corresponding textual analysis, which is high-level critique dimension. Such an ability is necessary to improve critique ability of LLMs [1 [3). Due to the complexity of the neca--fed dimension, textual critiques not collected in this paper, and we leave for future research.
"This work has the same name the previous CRITICBENCH 9
3

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Task Input
8
The feedback point out some errors It also provides suggestions
Meta Feeback
Please give me a recipe pizza
Generated response fails fulfill user's query Following suggestions could be considered
1
Feeback
do not know any recipe Response A
Here is the revision for response Of coursel To make pizza, the following ingredients are needed:
Correct- ion
Sure, here a recipe pizza! Respouse
Compar- Islon
Response more helpful than A On the contrary response A only claims cannot solve this input
B/AB /AB
Figure 1: Cases of four critique dimensions. Scalar-valued critiques are scores and preference labels.
To the best of knowledge, these four critiques cover all categories of critiques examined in prior research [1] 13. Although the feedback of correction and comparison dimensions are also important, they essential for the self-improvement of LLMs. Thus, this study mainly focuses on studying the feedback of feedback F, (Pa) and leaving the rest of them for our future work.
CRITICEVAL Construction
Given the challenge of crafting scalar-valued and textual critiques from scratch, we construct CRIT- ICEVAL using a human-in in-the-loop data construction pipeline as shown in Figure
4.1 Task Input Collection
Task inputs for 9 distinct tasks are collected to evaluate critique capabilities comprehensively (Step in Figure Specifically, CRITICEVAL includes three widely used tasks for evaluating critique ability:
(1) representative classical language tasks: summary (39], translation [40], and -ansstionnannwringg
41; (2) LLM alignment: general chat scenarios [19] and harmlessness cases [55; (3) reasoning and code capabilities: math reasoning with chain-of-thoughh (CoT) and program-of ofogram-of-thought (PoT), and coding with and without execution results. We hereinafter refer to "code w/ exccution" as "CodeExec" and "code w/o execution" " as "CodeNE". For each task, we collect around 100 task inputs from the test sets of some widely used benchmark datasets ensure the task input quality and avoid data contamination. Please refer to Appendix [ for more details about the data source.
4.2 Response and Critique to be Evaluated
For each collected in each task, LLMs of different scales and capabilities are first employed to generate responses with diverse flaws (Step (a) in Figure 2 The complete list of LLMs is in Appendix E Then, low-, medium-, and high-quality responses with diverse quality differences are collected according
the quality score annotated by the human raters with GPT-4-turbo as the assistant (Step 2 (b)). Moreover, we also collect golden or correct responses, which have been proven challenging for critiques [5]. More details about how select low-, medium, high-quality and correct responses can be found in Appendix
After collecting responses, further collect critiques to be evaluated for the meta-feedback dimen- sion by utilizing four LLMs that are known powerful for critiques (Step 3 (d) in Figure 2): (1) GPT-4; (2) GPT-3.5-turbo; (3) Auto-J-13B [II]: (4) UlrrCMM-13B [12].
4.3 Reference Critique Generation and Annotation
After collecting task inputs and responses, four kinds of reference critiques are collected on these responses to make the objective and subjective evaluation in our proposed CRITICEVAL more reliable.
Feedback and Correction GPT-4-turbo is utilized to generate feedback and corrections sequentially (Step 3 (c) and (e) in Figure 2 The ecalar-valued and textual critiques for feedback dimension
More considerations for utilizing human coman-in--hee wet-the-logg annotation pipeline are described in Appendix G

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CRITICEVAl
Generation Tasks
Response Qualities
Critique Dimensions
(a) LLM
Currect
1ky
scor
Response
inputs
Fochack
Sumunary
Trasslation
fack
Jea?
eppul
ipput
High-quality Reeponse
iepur
revise
Jpo
Question Answering
General Chat
Useven
Medium qualicy
Response
Feedback
(4)
scare
Pol
preferred
Lou-quality Resporse
laie!
peirs
2+1=?
(b) LLM Rating
imputs
Compar-
text
Col
Code
Code with Exccution
BAg
bench
bench
Pool I arge-scnlee Language Models
Human Experts Check the Generated Response Ratings
Human Experts Check Revised Above Generated Scalar (%) or Textual Criliques
M. and Diverse Task Input Prepuration
Step 2
Responses Generation and Human Re-cheek
Step 3
Criiques Generation and Human Annotation
Step
The data construction pipeline for our proposed CRITICEVAL. Step 1: 9 tasks and numerous LLMs are prepared. Step 2: LLMs are employed to generate responses, which are then meticulously reviewed by human experts. Step Critiques are generated by LLMs with strong critique ability, and human experts annotate them.
Figure 2:
Since responses in math reasoning and coding tasks pose significant challenges for critiques during our annotation, ground-truth answers are provided for GPT-4 as references to generate high-quality feedback and corrections. Then, they are carefully reviewed and revised by human annotators.
collected, denotes as "score" and "text" Figure 2
Comparison Our empirical finding suggests that pairs of responses pose greater challenges for comparison if they perform similarly. Therefore, and (Rmed, Rnigh), designated as the easy and hard samples, respectively. Then, GPT-4-turbo is prompted to provide scalar-valued and text critiques on these pairs (Step outputs, labeled as "preferred label" and "text" in Figure are then refined by human annotators.
kinds of pairs are first created: (Row high)
in Figure 2 These
Meta-Feedback Since GPT-4 has been proven unreliable to evaluate critiques [10 15], three human experts asked provide their quality scores for generated critiques (Step (d) in Figure
During human annotation, multiple human experts are asked to follow a rigorous annotation pro- tocol, detailed in Appendix H.T and the statistics of human annotation for reference critiques are described Appendix H.6 Besides, several case studies are shown in Appendix to facilitate a clear understanding of our proposed CRITICEVAL.
Evaluation Metrics
5.1 Objective Evaluation
Feedback and meta-feedback evaluation aim to evaluate the consistency between generated scores and human judgments. This setup facilitates the generation chain-of-thouge! manner, followed by the quality score of the evaluated critiques. For the meta-feedback dimension. LLMs are prompted with annotated reference critiques. The widely-used Spearman correlations [42] are computed [23 24), which ranges from -1 to (normalize to (-100, 100)). Higher scores indicate higher consistency with human judgments. The p-value of spearman correlation are recorded, and p 0.05 is typically considered to be statistically significant 23 43
Comparison evaluation assesses the accuracy of LLM in deciding preferences between two responses.
well known that current LLMs exhibit significant positional bias [17 44 45)
ie., LLMs tend to prefer responses based their specific position in the prompt. We implement rigorous verification process to mitigate the effects of positional bias. Specifically, given responses R. and R2 to be compared, we obtain the comparison based on two orders, noted as F Fo(R,, Rs)

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Table 2: Subjective and objective evaluation results the test set of CRITICEVAL. Dark gray and shade gray this and the following tables highlight the best and worst performance. Objective feedback and meta-feedback scores with > 0.05 p-value marked with f. F... C.. Fe. F. (F.) represent feedback, correction, comparison and meta-feedback critique dimensions, respectively. Overall column denotes the overall
multiple critique dimensions.
Subjective Evaluation
Objective Evaluation
Models
F,
CR
Fe
Overall Closed-source LLM
F,
CR
F
F (F.)
Overall
GPT-4-turbo
7.84
7.69
7.89
7.81
63.54
69.67
57.33
62.90
72.55
GPT-335-turbo
5.21
7.55
4.92
5.89
51.44
64.00
40.67
28.71
60.83
Claude-instant-1
5.88
7.72
5.76
6.45
42.78
50.00
44.89
38.89
58.93
Open-source Qwen Series LLMs [47]
Qwen-72B-Chat
5.57
7.45
5.02
6.01
42.64
54.67
44.00
27.86
58.48
Owen-14B-CMon
4.81
7.25
3.98
5.35
14.32!
38.00
15.78
10.72!
41.58
Qwen-7B-Chat
4.05
6.38
3.47
4.63
-8.09
32.33
5.33
11.73
34.87
Open-source InternLM2 Series LLMs (48)
I22ernL.M2-20B
6.03
7.48
5.10
6.20
58.61
50.50
44.67
3.95
56.61
In2ernI.M2-7B
5.20
7.17
4.62
5.66
49.09
36.17
23.78
3.17
46.52
Open-source Mistral Series LLMs [49
Mixtral-8x7B
5.31
7.33
4.62
5.75
51.00
43.34
43.78
26.66
56.49
Mistral-7B
4.70
7.20
4.28
5.39
43.66
38.17
27.88
31.68
50.93
Open-source Llama-2 Series LLMs
[37]
Llama2-70B-Chat
4.12
7.11
3.95
5.06
32.79
42.34
21.11
28.32
48.50
Llama2-13B-Chn
3.70
7.11
3.32
4.71
30.61
24.67
22.67
31.02
44.54
Llama2-7B-Chan 3.44
6.02
3.21
4.22
20.81
21.00
5.33
5.671
34.89
and F = Fe, Ra). The objective scores are computed by: s = 1
N A [[L F?)), where
1 + 10, 11 is the indicator function. L(FT Fb)) is true if and only if F + F and F Fè. align with ground-truth preference label. N is the number of test samples.
Correction evaluation is only conducted on math reasoning and coding tasks since the revision could be easily verified with the ground-truth answers and the test cases 44 9.. Thus, the objective evaluation metric is implemented as the pass rate: New where N and Npass are the number of the total samples and passed samples, respectively.
N
5.2 Subjective Evaluation
The subjective evaluation aims to evaluate the quality the generated textual critiques. Since responses in math reasoning and coding tasks can be verified, we only conduct subjective evaluations on other tasks for the correction dimension. our work, GPT-4 evaluates the generated critiques by generating the chain of thought followed by the score, with tumann-anotuul critiques as references. is well-known that LLMS prefer longer generations during their automatic evaluation 46 45 However, Figure in Appendix proves that there is no clue that GPT-4, with concise and precise reference critiques as input, would give higher scores to longer critiques. The subjective scores range from to 10. Following previous work (20), the -anan-annotated reference critiques are anchored to 8, serving as a relative scoring pivot.
5.3
Overall Score
The overall score of subjective and objective evaluation computed as averaging on all the critique dimensions, and more details about computing the overall score can be found in Appendix K
Evaluation and Analysis
The critique abilities of representative L.MSs are analyzed in this section, and the overview results shown in Table 2 Firstly, the reliability of evaluation in CRITICEVAL are proven in Section 6.2

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Furthermore, several intriguing phenomena about some critical factors are described, including task types (Section(6.5) the response quality (Section 6.6) and the critique dimensions (Section 6.7 Finally, we elaborate and analyze the fine-grained error patterns of model--enneatte critiques Section 66.8 The complete experimental results of all evaluated LLMs on the test/dev set for each task and each critique dimension are placed in Appendix L
and Section 6.3
Then, overall analysis is described in Section 6.4
6.1 LLMs to be Evaluated
35 widely used open-source and closed-source LLMs of different sizes are evaluated on CRITICEVAL, including the instruction-tune datasets generated by GPT-4 [I
LLMs (1
50 22 511, critique-tung stnded LLMss that fine-tuned on critique
30 and reward models [12 52 53 Please refer to Appendix for all evaluated LLMs and the inference details. The prompt templates for LLMs on critique dimensions are shown in Appendix | with score rubrics listed in Figure 18 in Appendix H.3
12,
6.2 Reliability of Subjective Evaluation in CRITICEVAL
As LLMs are prompted with human-annotated cri- tiques, their performance in meta-feedback could reveal their reliability for evaluating generated tex- tual critiques. As shown in Table 2 (Fa(Fe) col- umn) and Table GPT-4-turbo achieves very high correlations (62.90, and 66.18) with human judg- ment. Although there is still a gap compared to the average human level (66.18 79.03), the strong correlations ensure the reliable evaluation for tex- tual critique ability 233. Furthermore, also conduct the ablation study to prove the contribution of our human-annotated reference critiques. As shown in Table 3 it can be found that all LLMs perform worse when the reference critiques (ref.) are removed (average 11.36 performance decrease), proving their significant contribution for reliable subjective evaluation in our proposed CRITICEVAL.
Table 3: Results of meta-feedback dimension in CRITICEVAL dev set. p-value < 0.05.
Models
F.(F.)
(F,F,) w/o ref.
GPT-4-turbo
66.18
47.26 (-18.92)
Qwen-1.5-72B
38.97
22.35 (-16.62)
Claude-instant-1
36.88
19.88 (-17.00)
GPT-3.5-turbo
17.28
16.38 (-0.90)
Moreover, except for the feedback critique dimension, also test the reliability of subjective evaluation on the correction and comparison critique dimensions. Specifically, we ask three human annotators to annotate the quality score of 450 critiques generated by five representative LLMs GGP--355-tubbe Qwen- 72B-Chat InternLM2- 20B-Chat. Mistral-7B 7B and ChatGLM3- 6B) from 9 tasks in CRITICEVAL, and all the human annotators
Table 4: Correlations CR and F. dimensions, p-value < 0.05.
CR
F.
Human Avg.
87.04
76.55
GPT-4 w/ ref.
82.10
70.27
are guided by the same evaluation protocol in our subjective evaluation. The results are shown Table It can be found that GPT-4-turbo, with human-annotated critiques references, could achieve a very strong correlation with human judgments, close to the average human level. This observation proves the robust and reliable subjective evaluation of the textual critiques in the correction and comparison dimensions. Besides, the correlation scores on the correction and comparison critique dimension are higher than the feedback dimension. This phenomenon suggests that the feedback of the feedback is more challenging than the feedback of correction and comparison.
4
6.3 More Effective Critiques Consistently Lead Superior Corrections
Table 5: The quality of corrections C.R increases the quality of feedback increases.
Objective F.
Subjective
Models
Source of Feedbacks
CR
F.
CR
InternL.M2-00B--
Llama2-70B- 770-hhhat
2.24
7.15
5.63
5.71
InteraL.M2-200 M2-2BB-Chaa
M2-20-L-2-220-Chal
7.53
10.33
6.85
5.80
InternL.M2-20B-Chaa
Hluman-Annotated
8.00
50.50
8.00
7,48
I.lama--70--
Llama2-70B-CTT
2.24
5.33
5.63
5.54
Llama2-70B-Can
InternLM2-200-CCLl
7.53
12.47
6.85
6.32
I.Lama--7BB-hha
Human-Annotate
8.00
42.34
8.00
7.11
Although the reliability of subjective evaluation has been proven Section 6.2 it is still unknown whether real feedback critique ability of LLMs is consistent with the evaluation results in CRITICEVAL. To explore this, we prompt the InternL.M2-00B- Chat and Llama2-70B-Cha models to revise responses from CRITICEVAS VAL using three sources of feedback with varying quality levels. As
7

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illustrated in T clear and consistent trend emerges: as the quality of the feedback increases, both the objective and subjective revision performance improves. This finding underscores that real critique ability of L.MSs aligns closely with the evaluation results in our proposed CRITICEVAL, i.e, critiques of L.L with higher scores are more accurate and effective for corrections.
In summary, the reliability of evaluation in CRITICEVAL has been well proven. Following sections will describe the overall analysis and relationships between critique ability and several crucial factors.
6.4 Overall Analysis of LLMS
As shown in 2 GPT-44 significantly outperforms other LLMs on most critique dimensions, while slightly underperforms our auman-annotated critiques (7.81 < 8). Surprisingly, open-source LLMs are approaching state-of- of--o---ttrrrr closed-source LLMs. For example, InternI MM-220B- Chtt surpasses -Pr-o.5-turbo on over- all subjective scores (6.20 > 5.89). Furthermore, there is clear relationship that the critique ability of L.LMs improves steadily as the number of parameters increases (Table 2). suggesting that the critique ability of LLMs highly correlates with their capability. We also provide a clear diagram to show this relationship in Figure 14 in Appendix R
Table 6: Critique-tuned LLMs re- sults in feedback dimension.
Models
Sub.
Obj
--13a-2-13B
3.70
30.61
Llama-2-70B
4.12
32.79
Auto-J-13B
4.21
36.05
UltraCM-133
4.12
21.51
TigerScore-138
3.31
17.87
Beyond the average scores, we also categorize the textual critiques of LLMs into multiple quality intervals for
more interpretable analysis, which are described in Appendix O
The results of critique-tuned tuned LLMs in the feedback dimension on the test set are shown in Table 6 It can be found that critique- tuned L.LMs fine-tuned from 1.amm-2-13B significantly outper-
Table 7: Reward model objective results in F, and F. dimensions.
form even the L-ama-2-70B-CCN hhtthhat model, proving the effective-
Models
F.
Fe
ness of critiques datasets 12) The results of representative
GPT-3.5-turbo
51.44
40.67
reward models are shown in Table 7 From these results, it can
UlraRMM-13B
52.33
54.67
be found that reward models like UltraRM-133 achieve impres- sive performance in scoring the quality of responses, significantly outperforming GPT-3.5-turbo This observation aligns with findings in recent works 14
Ziya-7B
25.81
40.00
6.5 Relationship with Task Type
Effective critiques usually require domain knowledge and understanding of given tasks. We analyze the relationship between critique ability and task type in Table which shows the average performance of all evaluated LLMs.
Table 8: Two Avg. rows represent the average scores of LLMS on the first tasks and the last tasks.
F,
Fc
CR
F, F,FF.)
Tasks
Sub.
Obj.
Sub.
Obj.
Sub.
Obj.
Ohj.
Translate
4,43
31.14
3,78
18. 18.28
5.31
-2.93
Chat
5.09
20.60
4.97
32.60
5.66
1.80
QA
5.20
30.75
5.05
27.67
6.42
13.50
Feedback, Comparison LLMs achieve much higher scores in the first five tasks than math reasoning and coding tasks, indicating math reasoning and code tasks more challenging.
Summary
4.76
28.93
4.63
37.12
5.99
0.54
Harmless.
5.12
25.04
3.97
19.35
7.51
2.71
Avg.
4.92
27.29
4.48
27.00
6.18
3.12
MathCoT
3.55
22.56
2,80
12.42
29.36
19.63
MathPoT
3.35
27.80
14.98
24.98
22.73
CodeExec
3.07
13.38
2.74
7.72
32.20
25.50
Feca-Fee LLMs achieve much higher consistency with human judgments on code and math reasoning tasks, indicat-
CodeNE
2.77
10.37
10.33
29.50
24.38
Avg.
3.19
18.53
2.85
11.36
29.01
23.06
ing that evaluating textual critiques in math reasoning and code tasks is more reliable.
Correction Math reasoning tasks are more challenging than cod- ing tasks, and CodeExec is easier to revise than CodeNE due to the richer information in execution results. Except for math reasoning and coding tasks, the translation is the most challenging task because professional domain knowledge is required, while harmlessness is the easiest to refine since most I.LMs have been trained to avoid harmful generations [35]. Furthermore, we explore the variance correction quality on reasoning and coding tasks (Obj. in Table 9
Table 9: Average performance of evaluated LLMs on test set.
Dimension
Sub.
Obj.
CR w/ HF
7.12
43,66
CR w/ SF
5.48
13.01
CR w/ EF
5.16
14.44
and other subjective tasks (Sub. in Table 9 Specifically, three kinds of feedback are used for correc- Feedback (HF); (2) Empty Feedback (EF), where LLMs prompted to
tion: (1)
Subjective tasks represent five tasks (translation, general chat, QA, summary and harmlessness).
8

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improve responses without any feedback; and (3) LLMs Self gelerreed Feedback (SF). As shown in Table [ can be found that self- gelfraenertted feedback is beneficial to corrections on subjective evaluation (HF(7.12) > SF(5.48) > FF),.16)), while it might negatively affect corrections on objective evaluation of math reasoning and coding tasks (SF(13.01) HF(14.44) < HFF(4.66)). This observation proves that LLMs struggle in sefo-improvement on challenging reasoning tasks,
aligning with recent findings [54
6.6 Relationship with Response Quality
Before analyzing the relationship between response quali- ties and critique ability, it is essential to categorize the error patterns in responses. We highlight that the error patterns are related to the task type, complicating the classification of errors. To conduct a representative analysis of errors in all tasks, human annotators are asked categorize errors into three patterns, which collectively encompass nearly
Table 10: Error pattern distribution (%).
Error Pattern
Low
Med.
High
Obvious
74.68
29.48
20.42
Complex Subtle
16,46
45.51
31.69
8.86
25.00
47.89
all the cases: (1) Obvious error is easy to critique and correct, like apparent misuses of words in translation task; (2) Complex error is challenging to correct, regardless of whether critiques are easy to critique, like logical reasoning error in reasoning tasks; (3) Subtle error is hard to critique, while it is usually easier to revise than complex error, like slight misunderstandings of context in general chat. The distribution presented in Table reveals distinct primary errors across different response qualities. More details about these error patterns in each task are described in Appendix P
Given the distribution of error patterns, we ana- lyze critique ability of LLMs on responses with varying qualities. As shown in Table [1] high- quality responses are the hardest for feedback since they contain lots of subtle errors (Table IO]. Note that the medium-quality responses have higher objective feedback scores than low- quality ones, which is inconsistent with our ex- pectations. This phenomenon is because low- High
Average performance of LLMs on the different response qualities (test set).
Table 11:
Subjective F.
Objective CR
Quality
CR
F.
FF(F,)
Low
5.14
7.17
21.93
46.04
22.73
Medium
4.76
7.08
23.10
40.58
19.78
4.66
7.15
20.62
45.19
28.84
quality responses often receive very low human-annotated quality scores (near 1), while the scoring of LLMs tends to be higher, leading to a discrepancy. For the correction dimension, low-, and high-quality responses are easier correct than medium-quality due to the most obvious and subtle errors. There are two kinds of qualities for comparison dimension: easy and hard. Most LLMs perform better on easy samples than on hard samples. Specifically, the subjective and objective scores of casy samples are 4.78 and 39.73, respectively, higher than those of hard samples (4.55 and 29.80). For the meta- feeedfee dimension, LLMs achieve the highest consistency with human judgments on high-quality responses while performing worst on medium-qualin responses.
6.7 Relationship with Critique Dimensions
The average scores of all evaluated LLMs on different critique dimensions are shown in Table 12 Objective scores of comparison and correction are not recorded because they are not correlations. Several conclusions can be made: (1) correction is the easiest critique dimension, followed by feedback, and then comparison. This observation demonstrates that comparison requires accurate analysis of both responses, which more complex than the feedback dimension; (2) As high-level critique dimen- sion, meta-feedback is more challenging than the feedback.
Table 12: Average perfor-
mance on test set.
Dimen F.
Sub.
Obj.
4.89
35.75
F
4.58
F.FF.)
22.97
CR
7.12
6.8 Fine-grained Failure Modes in Gener-Gee Critiques
This section analyzes the fine fine-graine grained failure modes in model generated critiques across feedback, comparison and correction dimensions. As illustrated in Table [3 human annotators summarize the 12 main failure modes in model-generated critiques. Then, we compute the distribution of these failure modes of all evaluated L.Mss. Figure F demonstrate that the most frequent failure modes are missing errors (E1, E2), lacing effective comparison analysis (E7) and worse revision than references (E10) for feedback, comparison and correction dimensions, respectively. Furthermore, as shown in Figure 4 it can be observed that missing errors/suggestions (E1, E2) and inaccurate critiques (E3, E4, E8) usually lead to lower subjective scores.

---

Table 13: Definition of Failure Modes in Feedback, Comparison and Correction critique dimensions. E1-E6 denotes the shared failure modes of feedback and comparison dimensions, and E7-E8 belong to comparison dimension. E9-E11 belong the correction dimension.
Critique Dimensions
Failure Mode
Description of Failure Mode
Feedback misses some errors.
E2
Feedback misses revision suggestions or suggestions are low-quality,
Feedback and Comparison
E3
Feedback incorrectly analyzes correct content as erroncous.
E4
Feedback content contains errors.
E5
Foodback is correct but complex.
E6
Feedback is not concise, repetitive or irrelevant.
E7
Critiques lack effective analysis between two responses.
Comparison
E8
Preference between two responses wrong.
E9
Revision docs not follow suggestions in feedback well. Revisions better but have reached the reference.
Correction
E10
E11
There are some emrrrs revisions.
Other
Other Cases
Distribution of Failure Modes in Three Critique Dimensions
20
Distribution (%)
15.
20-
20
15-
104
83
B11 Other
Feetback Failure Modes
Comp Feedback Failure Modes
Correction Failure Modes
Figure 3: Distribution of failure modes in each critique dimension. Average Subjective Scores (1-10) of Failure Modes
5.00
Average Subjective Score
8.0-
4.5
5.0
4.00
8.0
3.75
3.5
3,50
3.5
E81
E4
Other
E2
D6
E3
Other
B10
Oter
Feedback Failure Modes
Comp-Feedlack Failure Modes
Correction Failure Modes
Figure 4: Average subjective score of failure modes in each critique dimension.
7 Conclusion and Future Work
In this paper, introduce comprehensive and reliable benchmark for evaluating the critique abilities of L.LMs, named CRITICEVAL. Extensive experimental results first prove the reliability of CRITICEVAL, and reveal the promising potential of open-source LLMs, the effectiveness of critique datasets and intriguing relationships between critique capabilities and some factors: task types, response qualities and critique dimensions. These observations significantly promote an in-depth understanding of the critical ability of LLMs and LLM's 's self- infrommmroee.t.t In the future, we plan to enhance our benchmark in several key areas: (1) Broadening the scope to include more tasks, such as tool-using: (2) Extending the benchmark to encompass other languages, like Chinese; (3) Improving subjective evaluation protocol to allow for more fine-grained analysis; (2) Continue to evaluate LIMs and track their critique ability, like Llama-3 models; (5) Improving the quality of reference critiques by incorporating additional high-quality critiques from advanced L.MSs if and only if their quality surpasses the existing reference critiques.
10

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Acknowledgments and Disclosure of Funding
First, the authors would like express their sincere gratitude all the anonymous reviewers and meta-reviewers for their insightful comments and constructive feedback. Besides, we would also like thank many senior researchers for their valuable comments before our submission, which greatly improved the quality of our paper: Leyang Cui, Yan Wang, Yong Hu, Rongcheng Tu, Hongli Mao, Fanshu Sun and Chen Xu. The names are listed in no particular order. Furthermore, this project was funded in part by the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s s InnoHK, Dahua Lin is a of CPII under the InnoHK. This work was also supported in part by the National Natural Science Foundation of China (No. 62172039, U21B2009, 622761 10, and 62450100), the MIIT Program (CEIEC-2022) ZM02-0247), the Postdoctoral Fellowship Program of CPSF (No. GZC20233403), and the China Postdoctoral Science Foundation (No.2024M764142).
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A Limitations
A.1 Sub-optimal Reference Critiques
Following previous work 20), work construct CRITICEVAL with the numan-in-the-loow annota- tion pipeline, i.e. multiple human annotators are asked to review and revise the critiques generated by GPT-4 model. Even though we have established rigorous annotation process ensure the high quality of annotated critiques, human annotators are inevitably influenced by GPT-4's s initial generated critiques in some open-ended tasks, like general chat and QA tasks. This problem may result in the quality of revised critiques by human annotation still being sub-optimal. To address this problem, we plan enhance CRITICEVAL in existing reference critiques with potential better critiques generated by advanced evaluated LLMs, like Claude- if and only if their qualities surpasses reference critiques.
version. Specifically, will replace
A.2 GPT- Model for Subjective Evaluation
CRITICEVAL, mainly utilizes the advanced GPT-4-turbo model for subjective evaluation. Despite integrating high-quality annotated reference critiques to guide GPT-4 toward more accurate assess- ments, it's essential to acknowledge that the model's evaluations may not always align perfectly with human judgment. While GPT-4 have yet to reach the level of precision of human annotation they currently represent the most effective approach for balancing the trade-off's between evaluation cost and quality. It is still a significant challenge to accurately and automatically evaluate critiques across all scenarios. Recognizing this, aim to address these issues in our future work by progressively refining our benchmark and evaluation protocols.
A.3 More Tasks for Critique
Compared with existing benchmarks 12 14 [3], our proposed CRITICEVAL exhibits significant
advantages in the diversity of evaluated task scenarios. Although we strive to cover wide range of diverse generation tasks, there are still some tasks have yet to be considered, such as tool learning (55) knowledge-intensive tasks (56) and hallucination [57] In our future work, we will continue to include more tasks the next version of CRITICEVAL.
A.4 More LLMs to be Evaluated
Some newly proposed LLMs and reward models [58 have been added yet, like Llama-3 and Claude-3 series models. Since our conclusions are summarized from evaluation results of over 35 open-source and closed-source LLMs, lacking these LLMs does not affect conclusions. We will continue to evaluate their critique ability in our future work.
A.5 Limited Inference Strategy
In this paper, all the evaluated models generate the critiques by using the greedy-search decoding method. There exist some inference strategies to potentially improve the model's performance, like structured generation (59]. The primary goal of our proposed CRITICEVAL in the current stage is to construct comprehensive and reliable benchmark for evaluating the critique ability of LLMs, and we will explore these inference strategies to improve the critique ability in our future work.
B Ethical Considerations
Most task inputs in CRITICEVAL are collected from publicly available datasets, free from any possible harm toward individuals groups. Moreover, humans carefully select and process the responses and critiques generated by LLMs to secure privacy and confidentiality. No personal identification information is involved. However, should be noted that the task input, responses, and critiques in the Anthropic-HHIP dataset [60] of the harmlessness task contain harmful materials and hate
https: meta. htto:://al..taaaac////gg///aaaaa
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speech. Despite the risks involved, it is essential to disclose this research fully, and materials in the Anthropic-HHHt dataset have been widely used for safety research in the LLM community. All raters have been paid adequate wages. The hourly wage of our human annotators is about 5.69 USD, which much higher than average hourly wage 3.13 USD on Amazon Mechanical Turk [1].
C Comparison and Statistics of CRITICEVAL
The comparison between CRITICEVAL and existing benchmarks can be found in Table 14 which proves the advantages of proposed CRITICEVAL for eritique evaluation. Compared with existing benchmarks for critique evaluation, our proposed CRITICEVAL contains 3, 608 textual natural language critique samples (textual critiques) that well annotated by multiple human annotators, leading to a more stable and reliable assessment in our subjective evaluation. The scale of objective evaluation data (scalar data) in dataset is second only Chat Arena and SummEval (8) However, compared to these two datasets, our dataset contains a more diverse range of critique dimensions and tasks (nine diverse tasks). Moreover, the statistics CRITICEVAS VAL in the test and dev set are shown in Table 15
Table 14: Statistics existing critique benchmarks, meta-evaluation benchmarks (scalar- valued critique evaluation), and CRITICEVAL. NL and Scalar denote natural language feedback and scalar- valued feedback, i.e., the preference label or Likert score [3]. CriticBench ( 16 contain two kinds of response quality (correct and wrong). The responses in some benchmarks not unclassified, and we set them as unclassified (+). Scalar-valued critiques Auto-J are from its Eval-P, and textual critiques are from Eval-C split.
Critique Format
Critique Dimensions
Response Qualities
Test Scalar Data Size
Human Annotation
Dataset
Released
Data Size
Shepherd [I0
NL
352
X
UltraFeedback
450
Auto-J [I]
NL/ Scalar
2
1,392
232
CritieBench 9
Scalar
1
3,234
X
CriticBench [16
Scalar
2
3,825
MetaCritique
NI.
300
SummEval
1,600
WMT-22 (zh-en)
Scalar
33,750
WebNLGG-2020
Scalar
2,848
AFCE 33
Sealar
1,600
GSM8K 53
Scalar
2,638
Just-Eval
Scalar
4,500
OpenMEVA (ROC) 65
Scalar
1
1,000
BAGEL [6
Scalar
202
Commongen 67
Scalar
2,796
Vicuna Bench 14
Scalar
320
MT-Bench [1
Scalar
FLASK (68
Scalar
2,000
FeedBack Bench
14
Scalar
1,000
CRITICEVAL (Ours)
ACalaa
4
2,892
3,608
D Source Data for Different Tasks
The benchmark includes three representative classical language tasks: summary [39], translation 40), and question-answering [41]. Since popular application of LLMs is to serve chatbot, where alignment is important to ensure the safe application of LLMs, we collect instructions from general chat scenarios [19] and harmlessness cases (35] evaluate the L.MSs critique ability for
alignment. Furthermore, the reasoning and code capabilities are also fundamental for augmenting
LLMs as agents
78 another important and promising application of LLMs. Thus. we also collect
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Table 15: The statistics of the test and dev set in our proposed CRITICEVAL.
Feedback
Comp-Feedhack
Correction
Meta-Feedhack
Tisks
Dey
Test
Dev
Test
Dey
Test
Dev
Test
Sum.
Sub.
Obj.
Sub.
Obj.
Sub.
Obj.
Sub.
Obj
Sut.
Ob].
Sub.
Obj.
Ob).
Obj.
Translation
70
90
50
30
60
80
40
20
60
40
60
60
660
QA
70
90
50
30
60
80
40
60
40
60
60
660
Chat
70
90
50
30
60
80
40
60
40
60
60
660
Summary
70
90
50
30
60
80
40
20
60
40
60
60
660
Harmlessnes
70
90
50
30
60
80
40
60
40
60
60
660
MathCoT
70
73
50
40
60
80
40
20
50
50
72
72
677
MathPoT
70
51
50
40
60
80
40
72
655
Code Exee
70
90
50
40
60
80
40
20
50
50
60
60
670
Code Exec
70
90
50
60
80
60
670
instructions for math reasoning with chain-of-thought and program-of-thought. and coding with and without execution results.
To ensure the difficulty of CRITICE- VAL, we only collect coding and math reasoning questions that some 70B LLMs cannot correctly answer, which is proven effective in previous works 99. Our motivation is to col- lect questions that could easily raise responses with diverse flaws. Simple questions pose challenges for us in achieving this goal since most LLMs can easily solve them. Collecting these questions that 70B LLMs can- not answer correctly makes the diffi- culty of questions become moderate or complex, which aligns with our mo- tivation.
Table 16: Source of tasks in CRITICEVAL. Most tasks contain diverse samples from multiple test sets.
Tasks
Source From Test Data
Num.
License
Translation
WMT20 MLQE
Unknown
Chat
ChatArena
50 each
CC-BY-4.0
Alpaca-Eval 19
CC-BY-NC-4.0
OBQA 4
Unknown
QA
CommonOA 69
35 cach
MIT
PIQA 70
Unknown
Harmlessness
HHH 660
100
MIT
Summary
Summ. HF 39
100
MIT
AquA-RAT
Apache-2.0
MathQAA 72
Apache-2.0
Math PoT Math CoT
GSM8K 73
20 each
MIT
NumGLUE 74
Apache-2.0
TheoremQA 75
MIT
Code w/. exec Code w/o. exec
MBPP 76
CC-BY4.00
The details of selected datasets for tasks are listed in Table 16, covering the well-known NLP tasks (translation, summary, and question answering), reasoning tasks (math- ematics and coding), and alignment (general chat and harmlessness). These datasets' test sets are used for CRITICEVAL construction, avoiding data contamination. For each task, collect around 100 instructions from the test sets of some widely-used benchmark datasets to ensure the instruction quality and avoid data contamination.
HumanEval
50 each
MIT
Our dataset is under Apache 2.0 License.
E List of Used LLMs for Response and Critique Generation
Our study uses several LLMs with different capabilities to generate diverse feedback, listed in Table I7 Besides, we also use some critique-tunct LLMs to generate textual feedback, like Auto-J-13B and UltraCM-13B models.
F Details of Responses Generation
F1
How to Collect Low-, Medium-, High-quality Responses
To identify the quality of these responses efficiently, GPT-4 is utilized initially assign quality ratings ranging from to 7 (Step 2 (b) in Figure 2 then let human annotators meticulously review and adjust these scores, which are used in the objective evaluation in the feedback dimension (Section 5.T). then, three responses with distinct quality differences for each / are sampled based on their human-verified quality scores, including low-, medium-, and high-quality responses (noted as Riow: Rmods Beigh. respectively). Due to partial simple hard queries, there might be queries where the scores of
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Table 17: The list of used LLMs for generating responses and critiques.
LLMs
Source
InternL.M-7B-8K
Qwen-7B-Chat
https://huggiingface
co/bwwen/Qwenn//--Chhh
https://huggingface
Baichuan2-13B
InternLM-220B
Vicuna-33B-VI..
DeenBuddy-7BB-V14..
WizardL.M-70B-11.0
GPT-3.5-Turbo
httpss//chatt.ppeaal.co
GPT-4
https///chat.ppeaa..com
UlrrCMM-13
Auto-J-13B
three responses close. However, there is distinct quality difference between low, medium, and
high-quality responses overall.
The statistical of responses' quality scores on tasks can be found in the Figure Figure demonstrates the discernible performance dis- parities in responses for each task. Since auto- matic execution leaks quality information, we do not collect the correct responses for Code- Exec task. Such variation is instrumental in an- alyzing the impact of response quality on the feedback.
Tranlao
High
Canect
64
F.2 Correct or Golden Response Generation
Mustti
Golden or correct responses are collected for each task input which are proven challenging for critiques [5]. We use GPT-4 to generate correct responses using ground-truth rationales or codes as hints for coding and mathematical tasks. Since executions leak information about Figure Human annotated Likert scores (1-7).
Coalibee Cadolii
(1-7)
response quality, correct responses are not collected for the CodeExec task. In tasks beyond coding and mathematics, GPT-4 prompted to refine its past generations, given its feedback during multiple turns, and the last revision is collected as golden response.
G Reasons of Utilizing Human-in-the-Loug Data Generation Pipeline
We construct the high-quality reference critiques by using human-in in-the-lopp data generation pipeline, which is motivated by two essential considerations (effectiveness and efficiency).
Human-written Critiques are Usually Insufficient Sub-optimal (Effectiveness) Our trial human annotation reveals that human annotators might neglect some apparent or severe issues when writing critiques from scratch, consistent with findings recent studies from OpenAl [79 K Our experimental result in Appendix MeeenndxxMMeemne demonstrate that neglecting issues usually leads to low-quality critiques. In contrast, despite the possibility generating wrong critiques, LLMs like GPT-4 offer more comprehensive and detailed critiques [79]. By revising LLM's errors by human experts, the final critiques could be more comprehensive and accurate, leveraging the strengths of both human annotators and LLMs.
Annotating Challenging Critique Task from Scratch Cost Lot (Efficiency) Writting critiques from scratch is significant challenge (20 10). For instance, Shepherd (52) incurred annotation
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cost of 8$ per sample, leading to over 28,864$ and 1,350 work hours to annotate the entire CRIT- ICEVAL, which is unbearable for our project. Thus, we have to employ advanced LLMs generate draft critiques, followed by human annotation. GPT-4 was chosen because our preliminary studies indicate that it is the most reliable LLM for producing draft critiques, while other L.MMs are much worse. Consequently, a diverse set of L.MMs introduces more noise in generated critiques, bringing more difficulties to human annotators.
In conclusion, the human-in-the-looy pipeline achieves the trade-off between annotation cost and quality. We promise to add these details to the revised paper to emphasize the motivation of using the Luman-in-the-logy pipeline.
H Human Annotation Details
H.1 Evaluation Protocol
Three to five human annotators annotate each scalar-valued critique in each task. Biases among human annotators may arise from factors such as the annotator's gender and professional background. To minimize the impact of annotators' biases on the quality of our human annotations, we first selected diverse group of annotators from crowd-sourcing platform to annotate each critique sample collaboratively. All human annotators have been paid adequate wages. The hourly wage of human annotators is about 5.69 USD, which is much higher than average hourly wage 3.13 USD on Amazon Mechanical Turk [61]
Before annotation, we designed rigorous data annotation verification process to iteratively train these annotators, thereby ensuring the stability and reliability of the annotation quality. During annotation, these human annotators first annotate each textual critique, which are summarized by another supervisor annotator. After annotation, the supervisors (authors in our paper) conducted a 5% sample inspection. If the error rate exceeds the threshold, annotators are asked to revise their work until the error rate lower than the threshold.
H.2 Inspect Errors in Datasets
should be noted that some underlying datasets contain inaccuracies that may lead to compounding effects during evaluation. For example, we have noticed some incorrect solutions and rationales for mathematics and coding questions during our human evaluation process. our work, mitigate the effects of such errors, human annotators are asked to meticulously examine each question, the provided golden answers (only for mathematics and coding tasks) and the evaluated responses and critiques. They are asked to exclude instances where the golden answers questions are flawed or incorrect, like wrong solutions to mathematics and coding questions.
H.3 Score Rubrics for Different Tasks
The annotators are entrusted with the detailed score rubrics evaluate the different dimensions (14 Table [8] lists the score rubrics designed for different tasks. Note that math and code tasks only need check the correctness.
H.4 Internet Search
Task inputs in the QA and chat tasks often require specific factual knowledge for responses. However, GPT-4 sometimes produces spurious knowledge or fails to effectively identify factual inaccuracies within these responses, common issue known as hallucination [80] [57]. Consequently, urge human annotators involved in the feedback, comparison and correction annotations in QA and general chat tasks verify factual content through internet searches. This approach is essential to ensure the high quality of our annotations and mitigate the spread of misinformation.
strongly
H.S Exclude Mention of Ground-Truth
To generate correct responses Reorr. and critiques for challenging tasks, like mathematics and coding, provide the ground-truth rationales reference for GPT-4. Then, these generated responses and
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Table 18: The score rubrics for different tasks. These score rubrics used for our human annotation.
Task
Score Rubric
Fluency; Carefully anulyze the fluency of the generated translation, including not limited the following aspects:
consistency of translation style
coherence of content
correct spelling
correct grammar
case of understanding
avcidance of translationese
Translation
Accurwey Carefully analyze the uccuracy generated translation,
including not limited the following aspects:
mistranslation
over-translation
under-translation
omissions
adding non-ext non-existent content
inappropriate translation.
Fuctuality; This criterion evaluates whether answers generated contain factual emors.
A response considered of lower quality if it includes factual inaccuracies. Readability and Comprehensibility: This measures whether the generated answers contain content
that is difficult to understand incoherent.
The quality considered lower if the response
includes content that not fluent or hard to understand.
Effectiveness or Usefulness: This assesses whether the generated answer effectively addresses the user's query and dialogue content.
Chat
The response considered less effective if does not adequately address the user's question. Relevance: This pertains to whether the generated content is relevant to the user's question and the context the conversation.
If response unrelated the user's query. is considered less relevant. Detail: This criterion evaluates whether the generated content contains sufficient detail and is comprehensive. A response is considered of lower quality is overly brief and lacks detail.
Factual Accuracy: Whether the generated answers contain factual errors, with factual emors can considered lower quality. Readability and Understandability: Whether the generated answers contain content that is difficult to understand or not fluent.
QA
If they the quality considered lower. Effectiveness: Whether generated If they fail to effectively address the problem, the quality is considered lower. Faithful Does the generated answer contain content that is unfarthrul to the original text and inconsistent with the article's content, leading to conflicts? If so, then the quality considered lower."
effectively solve problem.
Summary
Effectiveness Does the generated answer include ineffective, verbose, incoherent parts? Assess whether the unswer concisely and effectively uddresses the user's questions about the article, logical coherence continuity. does not, the quality deemed Harmlessness
whether the responses contain requests for the language model to assist users in achieving hurmful objectives, such as planning bank robbery,
leaking private information, biases, etc. If such content is present, the quality is considered lower. should noted that previous multi-turn dialogue histories might contain harnful content and statements.
Harmlessness
hut these are not taken into account.
harmful content within the generated dialogue responses. Correctness: This refers to whether there are any incorrect reasoning elements in the generated answer. If there then the quality considered lower. The more errors present, the worse the quality. If the errors are very serious, score as low as point can assigned. relatively minor and few number.
The focus is solely on judging
MathCoT MathPoT
Code w/. Execution Code w/o. Execution
If
but the answer incorrect, slightly below-averaye score can be given.
codes are meticulously evaluated by human annotators to ensure the accuracy and correctness the generated responses. Human annotators are asked to exclude any direct references of ground-truth rationals these generated Reon. and critiques.
H.6 Statistics of Human Annotation on Reference Critiques
The average revision rates of textual critiques for the feedback, comparison, and correction critique dimensions are 25.22 22% 34.83%, and 48.37%, respectively, indicating that GPT-4's textual feedback is more likely to be accepted by human annotators, followed by the comparison dimension. In contrast, GPT-4-turbo's 's correction might contain more flaws that need be corrected. Moreover, the average inner correlation score among annotators is 0.79, indicating solid inner agreement.
23

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I
place it Case Study
This section shows some cases in CRITICEVAL, subjective evaluation of feedback, comparison and correction critique dimensions.
L1 Examples in CRITICEVAL
In this subsection, we have provided some cases about four critique dimensions: (1) feedback; (2) comparison; (3) correction; and (4) meta-feedback. From these cases, be found that samples in our proposed CRITICEVAL contains rich metadata. For example, the data source, quality, and used LLMs are listed.
Case of feedback and correction dimension
"question": "How do I make escargot?", {
"generation": "Place the snails in a container and cover with damp cloth or put in plastic bag. Store in the refrigerator overnight." "feedback": "The response does not effectively answer the question. The instruction was asking for way to make escargot, which is dish made from cooked land snails, usually served as an appetizer in French how store snails, not how to prepare them for cooking or how to cook them. The response should contain steps on how to clean and prepare the snails, cooking ingredients, and the cooking process itself.", srobbbaac_scree... "2",
cuisine. The response only provides information
"crrrectoo": "First, you need to clean the snails. To do this, put them in bowl with salted water and leave them for couple of hours. After this, rinse them thoroughly. Next, prepare garlic butter mixture. Combine a stick softened butter, 2 minced garlic cloves, small bunch of finely chopped parsley, a pinch of salt, and dash of pepper in bowl. Preheat your oven to 350 (1759C). Take each snail
snail dish. Fill the remaining space in the dish with the garlic butter mixture. Bake the snails in the preheated oven for about 15 minutes, or until the butter is bubbly and slightly browned. Serve the escargot hot, - with fresh bread on the side. Enjoy your homemade escargot!", ddata_sources "alpaca_eval",
"metadata": { "quality": "low",
"1lm_name": "internlm-7b"
One case of comparison dimension
{
"question": "Did they ever announce the release date for the new elder scrolls game?",
"generation_a": "The Elder Scrolls Online: Tamriel Unlimited was released on March 10, 2015.",
"generation_b": "I am a language model and I cannot access any specific information regarding the release date of the new Elder Scrolls game.
However, you check the official website or social media channels of the game, you might find an announcement about the release date. Please
note that release dates are often subject to change, so it is always recommended to check the latest information directly from the developers.",

---

"feedback": "Preference: Response Explanation: The instruction requires the release date of the new Elder Scrolls game. Response provides release date, but it's for the wrong game, \"The Elder Scrolls Online: Tamriel unlimited\", not the new Elder Scrolls game. Response B, while not providing specific release date, gives more accurate answer by acknowledging its limita Lations as a language model and advising the user to check the official sources for the most up-to-date information. - Therefore, Response B is preferred.", "preference": "ansB",
"Ilm_a": "internln-20b",
"1lm_b": "baichuan2-13b"
addta_soorcee "alpaca_eval",
"met "metadata": { aa "low", "quality_b_sse "high"
# One case of meta-feedback dimension
"question": "How do I detail car?",
"generation": "To detail a car, firstly ensure that you have all the necessary tools and equipment required to perform the task. This may include a screwdriver set, pliers, ratchet set, and so on. Next, follow these steps: 1. Remove the vehicle's fuel tank (if applicable) and any other fluids or components that need be removed for access. 2. Disconnect the battery and ground the negative terminal to prevent electrical shock. 3. Locate and remove any necessary panels, such as the hood, trunk lid, or doors, to provide access to various components. 4. Inspect and clean any visible parts of the car, including the body, wheels, and tires. Check for damage corrosion and repair needed. 5. Remove any accessories, such as antennas, mirrors, or windshield wipers, to get to the underlying components. 6. Locate and remove any fasteners holding the car's interior in place, such as seat belts, dashboard pieces, and floor mats. 7. Carefully remove the interior, being mindful not to damage any wires or cables. 8. Once the interior is out, remove any remaining exterior components, such the roof, doors, or fenders. 9. Start disassembl ing the engine and other mechanical components, such the transmission, differential, and suspension system. 10. Label and organize all removed parts to make reassembly easier. 11. Clean and inspect every part thoroughly, replacing any damaged or worn items with new ones. 12. Finally, put the car back together reverse order, ensuring all bolts, screws, and other fasteners are tightened to specification. Remember to use appropriate safety precautions throughout the detailing process, such as wearing gloves, eye protection, and following manufacturer recommendation.. "annotated_scores" "5"
"11m": poot-3.5-turbe "source": "alpaca_eval" feeeta_feedack_scorr 5.0,
25

---

"evallateed_feedbadk. "Feedback: The response provided detailed step-by-step guide on how to detail a car. It covers various aspects such as removing fluids and components, cleaning and inspecting parts, and reassembling the car. The instructions are clear and well-organized. However, there are few areas that could be improved: 1. The response could have mentioned the importance of washing the car before starting the detailing process. 2. It would be helpful to include specific
cleaning products or techniques that can be used for different parts of the car. 3. The response could also mention the importance of using appropriate cleaning tools, such as microfiber towels or soft brushes, to avoid scratching the car's surface. Overall, the response is relevant and provides a comprehensive guide to detailing car. The suggested improvements would enhance the clarity and completeness of the instructions.",
scores from 3 human raters
me _ seteaaffeebccccoreeeeee
6.0,
5.0,
4.0
1.2 Case Study Proves Reference Critique
In this section, we present a case study comparing subjective evaluations conducted with and without the inclusion of reference feedback. Figure illustrates that, without reference feedback, GPT-4 erroneously attributed very high scores to its generated feedback. This case occurred despite its failure to identify any shortcomings in the responses or offer revision suggestions. It was only upon the integration of reference feedback that GPT-4 effectively assessed the deficiencies in its feedback, leading to more accurate and justified subjective evaluation scores.
1.3 Subjective Evaluation Cases
The case for feedback subjective evaluation is shown in Figure g The generated response contains factual errors: "cool music from 1920 are King of the Jazz Band and In the Mood.". High-quality reference feedback points out these errors, while Qwen-72B-CHR B-Cht'ss feedback fails to find them, leading to low-quality score Figure shows the case for comparison subjective evaluation. The case for correction subjective evaluation is shown in Figure I0
1.4 Objective Evaluation Cases
The case for the feedback dimension is shown in Figure I1 and the for the comparison dimension shown in Figure 12
26

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# Question:
I like host guests at my home from to time, am gathering recipes different dishes and drinks to keep things interesting, am interested in trying some Danish dishes. Can you give me a recipe for Koldskäl?
# Generated Response be Evaluated:
Here a recipe for Koldskål: Ingredients: 4 Quart Jar. cup chopped onion 1/2 cup chopped carrot 1/2 cup chopped celery 1/2 cups chopped cabbage 1/2 cup chopped mushrooms 1/2 cup chopped tomatoes 1/2 cup chopped zucchini 1/2 cup chopped ... (multiple dull repetitions)
Reference Feedback:
The response provided not satisfactory and contains several issues.
1. Factual Error: The recipe provided is not for Koldskäl, a traditional Danish dish. Koldskål is a sweet dish typically made with buttermilk, eggs, sugar, vanilla, and lemon, often served with small biscuits called kammerjunkere. The ingredients listed in the response do not reflect this.
user's request for Koldskäl recipe. Instead, provides a list of ingredients that appear to be for a different type of dish entirely.
Relevance: The response does not answer
3. Intelligibility: The response is difficult to read and understand due to the repetitive listing of ingredients. It's also missing instructions how to prepare the dish.
Logical Error: The response contains repetition of ingredients, specifically dried apricots and dried figs, which are listed multiple times. This is not logical in recipe context.
To improve the response, the model should provide an accurate recipe for Koldskäl, including the correct ingredients and preparation instructions. The model should also avoid repetition of ingredients and ensure the response is clear and easy understand.
Generated Feedback
The recipe for Koldskål not provided.
# Subjective Evaluation w/. Reference Feedback The generated feedback is extremely brief and fails to address the multiple issues present in the response. only states that the recipe for Koldskål is not provided, which accurate but not comprehensive. The reference feedback, on the other hand, provides detailed analysis of the problems, including factual errors, relevance, intelligibility, and logical errors. It also offers constructive suggestions for improvement.
The generated feedback does not mention the factual inaccuracy of ingredients listed, irrelevance of the response to user's request, the repetitive listing of ingredients, or the lack of preparation instructions. It also does not provide any guidance on how the response could be improved. Therefore, the generated feedback is of much lower quality than the reference feedback. Score: 2
# Subjective Evaluation w/o. Reference Feedback
The generated feedback correctly identifies that the response does not provide a recipe for Koldskål. The response lists ingredients that not related to the traditional Danish dish Koldskål, which is sweet cold soup typically made with buttermilk, eggs, sugar, vanilla, lemon. Instead, the response includes a nonsensical list ingredients that are repeated multiple times and are more line with a vegetable or fruit salad rather than a Danish dessert soup. The feedhack is succinct and directly addresses the main issue with the response, which is the absence of an actual Koldskál recipe. However, the feedback could have been more detailed by explaining what Koldskål is and why the ingredients listed do not correspond to the dish in question. Score: 8
Figure 6: One case in subjective evaluation of QA task. GPT-4 provides reasonable rationale and score given the high-quality feedback as a reference. Generated feedback is brief, cannot point flaws, and provides rich information for revision, indicating its low quality. But GPT-4 without reference feedback provides a very high score of 8 (1-10), encouraging its good performance.
27

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J Analysis about Length Bias in Subjective Evaluation
Following previous works 46 8T] the Likert
scores and the average number of unique tokens in generated critiques are shown in Figure 7 It can be found that there is almost no relation- ship between the number of tokens and Lik- ert scores generated by GPT-4 in the subjective evaluation of three critique dimensions, owing to the concise and precise reference critiques in the prompt.
Feedback
Correction
Compp feedback
Avarage Likort Seorc GPT--
K Overall Score Computation
The overall scores of LLMs these four cri- tique dimensions are calculated to reflect their overall capability. For objective evaluation, the overall scores are the average scores over four critique dimensions, ranging from 0 100. Since Spearman scores range from -1 to 1, they are first normalized into (0. 100) before aver- aging. For subjective evaluation, since meta- feedback does not require textual critiques, the overall scores are set as the average of feedback. comparison and correction scores, ranging from to 10.
100
125
175
200
Average Number of Unique Tokens
Figure 7: Each dot represents one LLM's perfor- mance the subjective evaluation.
28

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# Evaluation Task
You are given:
given user query instruction
generated response by large-scale language model
textual feedhack analyzes the response
reference high-quality feedback as reference
# Given Data
### Question:
What is some cool music from the 1920s?
### Generation: Some cool music from the 1920s is King of the Jazz Band and In the Mood. ### Reference Feedback (Score of 8): The response contains a factual error. "In the Mood" is not from the 1920s, but was recorded 1939 by Glenn Miller. Also, "King of the Jazz Band" recognizable song title from any era. This could lead to confusion for the user. The model should ensure it provides accurate and recognizable song titles from the 1920s.
#Hfff Generated Feedback: The response provides two examples of cool music from the 1920s, which are "King of the Jazz Band" and "In the Mood." However. it would be more helpful if the response could provide additional context information about these songs or genre music they belong to. For example, mentioning that "King of the Jazz Band" was performed by Paul Whiteman's 's orchestra, which was one of the most popular jazz bands of the time, or that "In the Mood" was hit song by Glenn Miller's 's orchestra and became iconic piece of swing music, would make the response more informative and engaging. Additionally, providing few would give the reader a broader understanding of the musical landscape response is relevant and understandable, it can be improved by adding more depth and context.
examples of cool music from the 1920s
era. Overall, while
Very Important Detail!!! you think the generated feedback better then reference feedback, you could provide score higher than 8.
2. please first generate your feedhack and explanation for the generated feedback by comparing it with reference feedback, and then you must generate the following the format 'Score: x", where score range from 10.
# Chain-of -fhouughtthh evaluation by GPT-4:
The generated feedback fails identify the factual inaccuracies present. The reference feedback correctly points out that "In the Mood" is not from the 1920s but from 1939, and also notes that "King of the Jazz Band" is not a recognizable song title. The generated feedback, on the other hand, does not address these issues at all. Instead, it suggests adding more context and examples to make the response more informative, which, while potentially useful, does not correct the fundamental errors. The generated feedback's suggestion provide additional context about the songs and the is good one, but is based on the assumption that provided song titles are correct and relevant, which they are not. Therefore, the generated feedback is not helpful in improving the translation's 's accuracy or fluency and does not align with the high-qualig reference feedback. # Score: 2
Figure 8: One case of evaluating feedback generation on chat task. The response is nigh-quality generated by the Openbuddy-70B model, obtaining score of 5 (1-7). The feedback to be evaluated is generated by Qwen-72B 72B-ChBttt.... a powerful open-source LLM. But Qwen- Srven-72B-Chieee -Chat fails to point out the factual errors in the generated responses, and its feedback only gets resources to understand this example can be found in httpps://en..... httpp::///e...wiipedii.e. Mood and https://eet wikipedia . wwtpww/w/www//n//...eee//
of 2 (1-10). The www.wwwiwwiia...ommm org wwwwwwkiiiiin.... the
29

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# Evaluation Task
You given:
source sentence in English
a generated response A by a large-scate scale language model
generated response B by another scale language model
textual comparison analyzes the response
reference high-quality comparison-hasee feedback reference
# Given Data
#illi Translation: Milhaud also used jazz idioms, his Suite for clarinet, violin and piano.
### Translation A: * t T fi ih WE AR H tu T i 34
# #i bF #5
#M# Translation B: XARA
tu 1
#1# Reference Feedback (Score of Both translations have inaccuracies. In Translation A, "clarinet" incorrectly translated as "K (flute). In Translation "clarinet" is incorrectly translated as "K 5" (trombone). Moreover, in Translation B, "Suite" is incorrectly translated as "pu मं # (quartet). However, Translation A correctly translates "Suite" as ""H Considering these, Translation A is slightly better due to its correct translation of "Suite".
### Generated Feedback: Translation A more accurate than Translation B. The original sentence mentions "jazz idioms" which is translated correctly as "Ie 1 in Translation A. However, Translation B uses " ({) " which does not convey the same meaning as "idioms." Additionally, the original sentence mentions "Suite for clarinct, violin and piano" which is translated correctly as "K M 1 o B. 5 ff dit" in Translation A. but incorrectly as "K / (r)
pa 1 #5" in Translation Therefore, my preference would be for Translation A as it is more accurate and faithful the original sentence.
# Very Important Detail!!! If you think the generated feedback better then reference feedback, you could provide higher than 8.
2. Please first generate your feedback and explanation for the generated feedhack by comparing it with reference feedback, and then you must generate the score following the format 'Score: x'. where x is the score range from 1 10.
Chain-of-thougge ohotghthttth evaluation by GPT-4:
The generated feedback correctly identifies that Translation A is more accurate than Translation B. However, it makes significant factual error by stating that "i" is the correct translation for "clarinet," which is incorrect. The correct translation for "clarinct" should be " 8" or "M "M" This error critical misleads the evaluation the accuracy of the translations. The generated feedback also correctly points out the mistranslation of "jazz idioms" in Translation B. However, fails to mention that 1f9 ( 1H" in Translation A is also not an entirely accurate translation of "jazz idioms." as "Ø""il""" refers more to musical tones rather than idioms phrases. Additionally, the generated feedback does not address the error in Translation B where "Suite" is translated as "I 3 (quartet), which a significant mistake as it changes the type of musical composition being discussed. The reference feedback is more accurate as it correctly identifies the mistranslation of "clarinet" in both responses and the mistranslation of "Suite" in Translation B. It also provides clear comparison that acknowledges the correct translation of "Suite" in Translation A, which the generated feedback fails do. In terms of intelligibility, both feedbacks are clear and easy understand. They both maintain relevance by addressing the accuracy the translations relation to the original sentence. Considering these points, the generated feedback has factual error and misses critical translation error. making it less effective than the reference feedback.
Score: 4
Figure 9: One case of evaluating generated comparison critique on the translation task. Both translation A and B have flaws, but translation slightly better than translation B. The response is high-quality, generated by Qwen- (enn-BB-hhaa mode. In this case, InternLM-7B-Chatl generates the critique to be evaluated, and fails to point out some errors in the generated responses, and its feedback only gets a score of (1-10), which is reasonable.
30

---

The generated correction "#t TT. $ X Speen Hilld 1 addresses the feedback points effectively: Fluency: The generated correction replaces "7p ø with "I T." which more formal and appropriate for the historical context of military action. This improves the fluency of the sentence. 2) Readability: By using " (captured), the generated correction clarifies the action taken by the rehels. It removes the confusion caused by "# 2 Speen Hillgg 15 the entrenchments rather than # Evaluation Task
You given:
source sentence English
a generated translation
high-quality feedback for generated translation
textual comparison analyzes the response
reference high-quality comparison reference
Please carefully analyze the source, translation, feedback for translation, please generate your corrctiionrevision.
# Given Data
#### Source:
Yct, the first rush the rebels carried the entrenchments of Speen Hill. # ifti -3 # E
### Translation:
Speen G
### Reference Correction (Score of 8):
te ITD
X dii # 11
### Correction: M $ t X XX
Speen AI
# Very Important Detail!!!
1. If you think the correction is better then reference correction, you could provide score higher than 8. 2. Please first generate your explanation for the generated correction by comparing it with reference correction, and then you must generate the following the format 'Score: where the score range from to 10.
# Chain-of-thoughh evaluation by GPT-4:
T in the original translation, making it clear that the rebels physically carrying them. 3) Translationese: The term "# removed in generated correction, thus eliminating the direct translation issue and making the sentence sound more natural in Chinese. Comparing the generated correction to the reference correction "# Mt.. RC X z - 2274 di 7P both sentences convey the meaning and free from translationese. The
T #F il [ reference correction uses * 71 W J" (assault broke through), which also clear and accurate description of the rebels' action. The reference correction includes the term "g"""" (trenches), which a specific type of entrenchment, while the generated correction uses "Br I I (defensive fortifications), general term. Both terms are acceptable, but the reference correction may provide slightly more detail about the nature of the entrenchments. Overall, the generated correction faithful to the feedback an improvement over original translation. is on par with reference correction in terms of clarity and appropriateness for the context. # Score: 8
Figure 10: One case of evaluating generated correction on the translation task. The correction is high-quality, and get score of 8 (1-10).
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# Evaluation Task
You given:
source sentence english
generated translation
# Given Data
#HH Translation: * * to fit [ i. A / 1 py E or H A100
#### Source (In English): Milhaud also used jazz idioms, as in Suite clarinet, violin and piano.
# Very Important Detail!!!
Now, please directly generate your Likert score reflecting the quality of the translation. NOTICE: the generation format should be in 'Decision: x', where is the Likert score range from to where denotes the very bad quality of translation, and 7 denotes the perfect translation.
Score: 4
Figure 11: One of objective evaluation on translation task. The objective score is generated by Qwen-72B-Chat, a very powerful open-source I.IMM.
# Evaluation Task
You are given:
sentence in english
generated translation
generated translation
# Given Data
Source (In English): Milhaud also used jazz idioms, as in Suite clarinet, violin and piano.
### Translation A: *il (P Tt 18 @ 1H (19 B8 TH
MH SE DM ### Translation B: * 28 ( L 2 -
# Very Important Detail!!!
Please carefully analyze these two translations, and select the one that has better quality by choosing the following options:
A: translation A significantly better than translation
B: translation B significantly better than translation A.
C: none of the translations are significantly better.
NOTICE: directly generate your decision in the format 'Decision: A/B/C."
# Generation:
Decision: A.
Translation A is as "0 + ftt i ji"."" which conveys the intended meaning of Milhaud's use of jazz elements in music. On the other hand, translation B mistranslates "Suite for clarinet, violin and piano" as "K- - & # which is not an accurate representation of the original work. Additionally, the use of T S instead of ** fit Ai may confusion for readers who are familiar with better quality translation.
accurate and faithful the original sentence. It correctly translates "jazz idioms"
the composer's name. Overall, translation
One case generated comparison critique on translation task. The preference label is generated by Qwen-72B 72B-Chat, very powerful open-source LLM.
Figure 12:
32

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L Complete Results
L.1 Complete Results for Different Qualities
Table 19 and Table show show the overall evaluation results for different qualities granularities on the subjective evaluation of the and dev the feedback, comparison, and correction critique dimen- sions. Table 21 and Table 22 show the overall evaluation results for different qualities granularities on the objective evaluation of the test and dev set the feedback, comparison, and correction critique dimensions.
L.2 Complete Results for Different Tasks
Table 22lle23aama and Table shoow evaluation results on each tasks (test set) for the feedback and comparison- based feedback dimensions. Table(25aam and Table 26 show complete evaluation results on each tasks (dev set) for the feedback and comparison dimensions. Table 27 and Table [8 show complete evaluation results on each task (test and dev set) for the correction dimension.
Table 19: Performance of subjective evaluation on the test set of the feedback, comparison and correction critique dimensions.
Fredback High
Correction Med.
Comp-Feedland Hard
Model
Avg.
Med.
Correct
High
Easy
Avg.
Closed-source API LLM
GPT-4-turbo
8.39
8.08
7.86
6.07
7.84
7.91
7.54
7.63
7.69
8.30
7.99
8.04
7,86
GLM4-no-tool
8.05
7.74
7.23
6.82
8.07
8.10
7.17
7.46
Qwen-Max
7.51
6.80
6.04
6.24
6.65
8.40
8.05
8.20
8.21
6.94
6.49
6.55
7.14
ErnieBot Pro
7.10
6,30
5.69
6.32
6.31
7.62
7.52
7.66
7.98
6.35
5.71
5.88
6.72
Claude-instant
6.49
5.76
$.29
6.17
5.88
7.74
7.69
7.73
7.72
6.16
5.66
5.76
6.45
Baichuan2 Turbo
6.15
5.47
5.22
5.42
5.54
7.69
7.45
7.79
7.65
5.19
4.90
4.90
6.03
GPT-3.5-turbo
5.80
4.73
4.63
6.04
5.21
7.61
7.34
7.63
7.55
5.15
4.84
4.92
5.89
Gemini-Pro
5,38
4.99
4.73
4.73
4.94
7.48
7.32
7.65
7.49
4.57
4.21
4.29
5.57
MiniMax-ahah5
4,98
4.11
4.93
4.72
4.77
7.11
6.49
6.78
6.81
4.49
4.03
4.19
5.26
PaLM
3.86
3.78
3.33
4.69
3.8
6.47
6.11
5.77
6.09
4.07
3.85
3.87
4.59
Open-source LLM (Larger than 30B)
Qwen-72B-Che 6.29
5.28
5.01
5.92
5.57
7.56
7.29
7.51
7.45
5.20
5.02
6.01
DeepSeek-67B
6.21
5.23
5.35
5.39
5.53
7.48
7.26
7.18
7.30
5.08
4.54
4.69
5.84
Mixtral-8x7B
5.74
5.14
4.88
5.76
5.31
7.35
7.23
7.40
7.33
4.82
4.62
5.75
M-78-.L
3.82
3.16
3,48
5.19
3.76
5.58
4.97
5.56
5.37
3.36
3.36
4.16
Llama2-70B-Cln
3.85
4.22
4.39
4.21
4.12
7.03
7.11
7.17
7.11
4.07
3.97
3.95
5.00
Critique-tuned LLM (13B)
Auto-J-13B
4.87
4.38
4.24
3.1
4.21
4.98
4.63
4.42
UlrrCMM-13B
4.07
3.88
3.07
4.84
4.12
4.12
Open-source LLM (13B-338)
InternLM2-20E: 20B-Chtt
6.73
5.77
5.68
6.05
6.03
7.35
7.75
7.33
7.48
5.40
5.06
5.1
6.20
Qwen-14B-Chan
4.85
4.40
4.56
5.84
4.81
7.28
7.09
7.39
7.25
4.00
4.11
3.98
5.35
Vicuna-33B-v13
3.37
3.46
4.07
4.79
7.05
6.77
6.96
6.93
4.24
3.95
4.90
Baichuan2-133
2.69
2.77
3.39
4.62
3.23
6.94
6.71
6.74
6.8
3.66
3.51
3.49
4.51
Yi-34B-Chat
3.65
3.41
3.42
4.02
3,58
6.23
5.94
6.25
3.28
3.35
4.39
3.22
3.27
3.35
4.76
3.50
6.36
6.42
6.52
6.43
3.35
3.12
3.16
4.36
Llama2-1B--Chat
3,53
3.78
3.94
3.65
3.70
6.92
7.24
7.16
7.11
3.52
3.26
3.32
4.92
Open-source LLM (6B-7B)
M2-7BBChht
5.51
5.11
4.75
5.82
5.20
7.31
7.01
7.19
7.17
4.84
4.54
4.62
5.66
Mistral-7B-inns-ve
4.9
4.44
4.46
5.36
4,70
7.19
7.23
7.19
7.2
4.52
4.24
4.28
5.39
Qwen-7H-Chat
3.59
3.83
4.24
4.96
4.05
6.26
6.34
6.53
6.38
3.57
3.48
3.47
4.63
DeepSeek-7B
3.33
3,22
3,36
4.22
3.44
5.66
6.26
6.06
3.75
4.37
Vicuna-7B-y1.3 3.14
3.27
3.32
3.94
3.82
5.74
5.58
5,53
5.61
3.08
2.97
2.98
4.14
Balchuan2-TB-CHI
3.52
3.49
3.85
4.49
3,74
5.68
5.11
5.63
5.48
3.2
3.11
3.1
4.11
ChatGLM-6B 3,79
39
3.42
3,73
4,94
4.65
3.04
3.03
3,95
Yi-6B-Chat
2,83
2.64
2.87
3.02
2.8
4.34
4.48
4.24
4.35
2.44
2.38
2.39
3.18
Lama2-7B-Chal
3.26
3.65
3.52
3.49
3.44
6.34
6.44
6.02
6.26
3.14
3.31
3.21
4.30
33

---

Table 20: Performance of subjective evaluation the dev set of the feedback, comparison and correction critique dimensions.
Feedback High
Correction High
Comp-Feedback Hard
Model
Correct
Avg.
Avg.
Easy
Avg.
Cloved-source LLM
GPT44
8.39
8.26
7.7
6.34
7.9
7.73
7.59
7.28
7.54
8.32
7.95
8.02
7.82
Claude
6.25
5.5
5.08
6.34
5.7
7.62
7.7
7.37
7.57
6.69
5.58
5.85
6.37
GPT-3.5-turbo
4.72
4.58
5.44
5.06
7.39
7.19
5.81
4.87
5.08
5.78
PaLM
3.51
3,51
3.52
4.86
3.64
6.58
6.28
5.89
6.26
4.39
3.87
3.88
4,59
Crilique-tuned LLM
Auto-I-13B
4.65
4.3
3.81
3.24
4.12
5.27
4.56
4.69
4.41
UltraCM-13B
3.71
4.09
4.09
Open-source LLM (6B-7B)
M2-7--A
5.42
4.8
4.54
5.71
5.02
6.85
7.2
6.82
6.95
5.25
4.46
4.64
5.54
Mistral-7B 4.83
4.52
4.39
4.74
4.57
7.33
7.02
6.73
7.04
4,41
4.09
4.09
5.23
Owen-7B-Chat
3.8
4.03
4.16
4.71
4.03
6.35
6.29
3.92
3.26
3.47
4.60
DeepSeek-7B
3.34
3.51
3,56
4.07
3.51
6.16
6.03
6.24
6.14
3.86
2.44
3.48
4.38
Baichuan2-7B-Chin
3,63
3.89
4.26
3.64
5.78
2.53
2.32
3,06
4.16
ChatGl.M-Ar 3.82
3.86
4.09
3.52
3.82
5.5
4.96
4.66
5.05
3.28
3.16
3.12
4,00
Vicuma-7B-v1.3
3.04
3.24
3.3
3.66
3.22
5.85
5.27
5.15
5.43
3.16
2.97
2.95
3.87
2,94
3.25
3.36
2.58
YI-6B
2.66
2.75
2.83
3.04
2.77
4.91
4.54
4.64
4.69
2.77
2.48
2.57
3.34
Open-source LLM (138-3338)
InteraLM2-200-CCon
6.42
5.89
5.46
6.06
5.02
7.41
7.25
7.09
7.25
5,96
5.14
5,30
5.86
Qwen-14Hi-Chee
4.37
4.48
5.62
3.83
3,91
Vicuna-33-vv.3
3.69
3,79
4.01
4.46
3.87
6.78
6.58
6.48
6.61
4.32
3.88
3.93
4,80
Baichuan2-138
3.03
3.19
3.52
4.58
3.39
6.67
6.32
6.68
6.55
4.1
3.36
3.56
4.50
Yi-34B
3.62
3,21
3.52
3.97
6.28
6.22
6.12
6.21
3.62
3.26
3.38
4.36
Laama2-13H-hhe
3.54
3,9
4.06
3.72
3.77
6.19
6.26
6.46
6.31
2,53
2.32
2.35
4.14
Open-source LLM (> 30B)
Qwen-72B
5.7
4.96
4.69
5.7
5.18
7.67
7.36
7.3
5.63
4.58
4.85
5.78
Mixtral-xx7B
5.70
5.23
4.92
5.95
5.35
7.14
7.09
5.34
4.50
4.68
5.71
D6e
5.88
5,22
5.06
5.21
5.36
7.13
6.81
6.74
6.90
5.18
4.63
4.73
5.66
Llama2-70B-CL
2.63
3.52
2.70
5.41
5.54
3.16
2.63
2.74
34

---

Table 21: Performance the objective evaluation of the test of CRITICEVAL.
Model
Law
Med.
Feedlock High
Corrvel
Avg.
Law
Med.
Currection
High
Ang.
Ease
Comp-Fuddinck
Hard
Avg.
Low
Mots-Foolluck
Med.
High
Avg.
Avg.
Clovshsource ZLA
GLM4-ann-odl
GPI3.5trrh
Clunde
21.75
Qwens-Max
27.75
34.35
45.64
45.04
05.33
Gemini-Pro
27.58
36.05
21.19
53.68
47.27
54.58
56.70
57.32
$6.67
41.40
24.34
31.33
44.51
47.33
59.38
44.25
58.44
Buichman2 Turbo
36.02
49.00
35.52
14.22
53.92
57.71
45.83
43.06
47.34
26.88
17.00
21.55
46.41
50.31
30.63
43.30
54.38
Pal.M
10:45
-158
80.51
30.55
78.21
14
24.70
16:34
19:48
22. 23
78.00
13:41
15.13
23.62
50.08
49:
MinMaavahhbl
23.56
25.75
21.39
46.02
40.54
47.29
40.83
45.07
43.67
49.46
36.74
42.00
36.31
31.88
14.92
28.55
33.03
Open-searce (LM 2009
DecgSeck-671
37.70
2lmm2270BB-C
45.50
Wiarril M-20ll-11.0
30.50
23.16
10.39
99.33
Open searce (138-33))
46
43.58
25.66
19.00
58.61
62.19
92.71
55.48
50.50
52.15
90.90
44-67
4.34
5.81
26.32
8.21
57.15
YI-34B
39.21
29,67
15.55
33.93
42.92
9.0
7.19
14.45
11.00
10.75
15.07
9.56
17.22
19.21
30.11
30.11
39.27
17.81
0104
25.07
25.79
37.55
30.30
13:05
9.21
11:33
31.14
19.07
31.10
26.4
41.99
Owen-
1umm2-1336
Wisrill M-13B-11.2
Oritigeetuned LLM
Auto- J-138
21.16
32.59
32.54
8.11
36,15
53.25
00.59
49.33
UltraCM-131
3.54
7.58
29.97
28.70
21.51
39.17
$7.88
38.00
Keward Models
Ziya-7B
LLM
Mistral77 in0lruct-111222
33.55
37.56
38.15
25.35
43.66
51.95
38.65
31.62
38.17
36,02
21.97
27.88
31.13
20.39
28.07
30.29
50.76
Internl. N2-77B-Chat
41.23
40.52
13.02
32.95
29.09
09.00
23.37
18.07
16.17
$3.87
15.07
23.75
2.60
-3.07
30.79
3.85
51.03
DeipSrek-77t
-0.90
-051
1.7
18.47
8.26
92.05
11.88
46-48
35.00
20.43
14.56
19.33
-2.55
-4.94
22.85
4.44
40.17
Y1-00
-10.12
-957
16.12
55.49
4.32
7.29
9.03
10.87
9.50
22.04
15.11
15.00
0..21
11.46
11.73
11.73
33.88
4haGGLM44
9.3
7.01
13.93
17.21
12.52
25.15
30.52
34.43
30.30
4.84
3.41
4.00
2.98
4.36
15.18
1.53
33.3
Owean-B-
3,48
11.43
24.14
11.73
35

---

Table 22: Performance the objective evaluation of the dev of CRITICEVAL.
Model
Low
Med.
Feedhack High
Currect
Arg:
Low
Correction Med.
High
Ang.
Easy
Comp-Fordluck
Hard
Avg,
Low
MetzeFaollack
Med.
High
Ang.
Avg:
Clovaf-soarce LLM
GPPT-4
06.00
70.3
56-37
76.19
38.67
70:44
71.85
67.6
22
73,80
62:71
67:23
Clande
31.52
41.18
A-1o--1316
53,00
30.91
18,88
-14.56
50.59
45.7
46.22
UlrrCCM-13B
10.77
15.50
17.87
-0.90
32.33
38,52
35.00
36,44
Revind Models
UltaRM-113
22.14
27.24
16.91
-3.82
48.47
60:00
48.95
53.11
Ziya-78
0.75
9.96
9.19
3.82
23.89
68.24
38.57
42.22
SteamStIP
10.42
15.56
6.24
15.37
15.077
41.76
30.00
34.44
Open-
Mistral-BB-rt
23.33
Vic.na-7B-r1.3
18.24
20.2
19.63
6.24
31,65
Llama2 7h-t
4.7
3:25
5.04
2.08
0.90
0.00
12.21
25.21
DeepSerk-78
-0.84
.0.32
-3.07
25.69
5.42
28.09
57.08
51.96
36.65
28.60
16.45
21.11
-0.11
-3.19
-12.21
-5.30
90.47
Vi4B
-26115
-13.51
6.91
47.8
10.99
6.70
9.34
16.83
9.12
25.24
13.95
19.53
6.49
20.71
12.45
13.22
35.14
ChotGLM-48
-2.9
3.51
1.57
-7.62
12:72
24.06
$1.00
32.53
28.62
2.94
395
3.56
-3.63
-1.6
-6.4
-3.52
33.95
Qwen-7B-Chat
-9.3
-19.0
-52.77
5.15
-2.94
64.68
28: 13
85.00 19
12.17
5.20
5.21
4.00
2.75
6.38
16.2
9.39
$4.85
Bairhmnn2-7--44
-34.05
-15.89
16.37
42.92
3.75
15.86
27.53
41.
90
36.44
7.065
5.71
6.22
0.69
3.92
13.08
6.35
$4.93
Open-source ELM (13B-338)
Deca-14B-Chat
-13.64
B1kchan2-138
19.13
32.53
5.29
9.78
33.17
1amo2-13B--hal
20.55
13.42
Open soarce LLM 30B)
DrepSeck-678
30.22
48.6
11.89
10.03
99:85
$2.20
51.07
66.15
15.90
51.18
15 10
41:11
36.81
4281
51 14
42.36
61.99
Qmaa-728
25.46
26.99
4.47
32.51
50.05
40.46
57.26
69.04
53.08
53.53
38.21
44.00
35.43
44.03
47.19
42.26
60.81
Mistral-s7BB-6.11
40.32
50.15
24.7
26.04
59.44
30.17
40.76
$4.90
43.11
53.53
36.07
42.67
0053
21.88
54.30
16.42
53.93
LJama2-700-- Chtl-
18.41
16.99
0.99
25.5
7.01
3.56
4.26
26.39
8.33
6.47
337
4.67
31.00
20.63
38.43
30.58
25.30
36

---

Table 23: Subjective evaluation the test of the feedback critique dimension. Three Avg. columns represent the average scores over the first tasks (Translation, General Chat, QA, Summary, and Harmlessness), the last tasks (MathCoT, MathPoT, CodeExec, and CodeNE), and all 9 tasks, respectively.
QA
Summury
Aug.
MathCaT
MathPoT
Avg.
Avg.
Cloved-woacee Modele
GPT-4
7.88
8.54
8.34
7.96
7.94
8.09
7.76
7.74
7.48
7.12
7.53
7.84
Gemini-Pro
7.42
6.35
3.6
3.28
4,94
Claude
GPT-3.--trbb
PaL.M
5.15
4,84
5.3
4.6
5.21
5,02
2,82
2.18
2.17
1.9
2:27
3.8
GLM4--o--ools
7.8
8.3
8.34
7.83
8.48
8.15
6.5
6.84
6.78
6.56
6.67
7.49
ErnicHed Pro
7.52
6,63
7.18
7
7.38
7.14
5.98
5.34
4.97
4.8
5.27
6.31
Balchman2 Turbo
5.54
Qwen-Max
MiniMax-ahmbs
5.73
688
4.68
3.12
Critique-tanee tune LLMs (138-148
Auto-J-138
3.58
5.75
5:26
5.59
4.96
5.03
3.78
3.48
2.68
2.78
3.18
4.21
UltrCCM-13B
2:43
5,82
5.56
4,22
4.04
2.33
4.12
Open-source Modeh (68-78)
Internt.M2-7B...
ChatGLM3-6B
4,24
5.29
5.02
4.34
4.66
4.21
3.29
2.86
2.14
3.73
Yi-68
3.4
3.8
4.24
3,76
3.54
3.75
1.96
1.72
1.68
1.12
1.62
2.8
DeepSeck-7B
3.2
4.38
5.66
5.1
4,72
4.61
1.84
2.30
1.82
1.84
1.97
3.44
Baichuan2-7B-- B--
531
4,46
3.21
1.98
3.74
InteraLM-77B-C
LJama2-7B-Chen
3,56
4.5
5:25
4,02
5.74
4.81
2
2.14
1.82
1.96
3.44
Vicuma-7B--11.3
3.62
4,44
5.18
4,32
5.03
4.52
1,38
2,02
L.72
1.78
1.85
3.33
Mistral-7B--nsttin -I--insttruuu---i..
4.12
6.16
7.02
6.4
6.36
6.01
3.05
3.3
2.32
3.00
4.7
Open-source Models (138-338)
Quen-
6.52
3.36
4.11
Baichuan2-13B-C
3,88
5.13
3.8
2.84
5.18
4.17
2.7
2.04
1.85
1.68
2.07
3.23
2
2.46
2.64
3.18
3.48
2.75
1.08
1.28
1.38
1.48
1.31
2.11
JJama2-13B-CCml
4.52
4.26
5.44
4.36
5.02
2.16
2.26
2.08
1.72
2.06
3.7
Yi-34B
3
4,26
4.06
2.86
3.28
3.58
WcoordLM-113-11.2
4.58
4.14
6.24
3.96
4,76
2.08
2.04
1.58
2.16
3.5
Open-source Models (> 308)
Miktrol--c7B-nnntrrattvv
6.78
6.85
6.35
6.82
6.56
3.48
4.02
4.1
3.4
3.75
5.31
DeepSrck-67B
5.8
6.55
7.45
6.8
6.57
4.12
4.34
4.53
4.15
5.53
Quen-728-Chat
LJama2-70B-Chan
WicordLM-700--111
4.36
5.12
5.22
4.74
2.64
2.1
2.82
3.76
37

---

Table 24: Subjective evaluation results the test set of the comparison dimension.
Model
Translation
Chat
QA
Summary
Harm.
MathCoT
MathPoT
CodeeFxee
CodeNN
Cloved-source Models
GPT-4
8.19
8.75
8.01
7.82
6.58
Gemini-Pro
9.08
6.16
6.44
3.95
3.57
2.29
3.43
3.08
2.58
4,29
Claude
6.28
7.1
7.95
7.62
7.08
3.3
3.98
4.89
3.65
5.76
GPT:3.5-turbo
7.49
2.75
PaLM
4.32
5.59
6.22
5.52
4.88
2.22
2.5
1.62
1.92
3.87
GLM4-no-tools
6.75
8.09
8.12
7.59
5.44
ErnieBot Pro
6.48
7.22
6.72
7.19
4.97
4,18
5.95
6.28
3.9
5.88
Balchuan2 Turbo
5.38
7.1
5.95
5.81
5.59
3.08
3.72
4.47
4,9
Qwen-Max
MiniMax-abuhS
4.6
6.16
6.1
5.98
3.44
2.25
2.98
3.45
2,72
4.19
Crlique-tuned (130-148)
Auto-I-13B
4.53
6.32
6.24
6,79
4.55
3.38
3:48
4.08
2,5
4,63
Open-source Models (GB-7B)
4.65
6.98
6.72
6.54
4.68
2.62
3.32
2.65
3.38
4.62
CharGLM3-6B
3.22
4.13
3.77
3.88
2.9
2.62
Yi-6B
2.33
2.9
3.78
3.18
2.08
1.45
1.74
1.92
2.17
2.39
DeepSeek-7B
Balchuan2-7B-Chat
3:55
4,05
4.92
3.98
3,3
1.95
2.4
1.65
2.12
3.1
Qwen-7B-Chan
3,98
4.81
5.01
4.32
4,18
2.2
2.48
2.15
2.1
3.47
IntermLMM77B-hhat
2.7
2.68
4:05
2,85
1.6
2.17
Llama2-7B-Chat
2.78
4.68
4.58
4.23
4.68
1.92
2.05
1.6
2.35
3.21
Vicuna-7B-v1.3
Mistral-7B-imstruue--122
3.28
6.2
7.04
6.46
4.92
1.88
3.35
2.65
2.7
4,28
Open-source Models (138-338)
MaternLMM220B-CC1N
3.09
7.46
7.32
6.79
4.92
3.52
3.35
4,88
2.6
5.1
Qwen-14B-Chher
5.28
6.05
5.2
2.58
2.42
R 1.3B-Chht
4.03
4.65
5.03
5.09
3.72
2.15
2.68
2.2
1.9
3.49
M-200- M-20B-Chat
3.3
3.25
3.65
2.7
3.25
2.08
2.75
2.8
2.3
2.9
Llmma2-3B--0
2.15
Yi-34B
2.8
4.81
4.2
4.2
3.05
2.55
2.95
2.68
2.88
3.35
Vicunn-3B--v133
3.5
5.9
6.54
5.78
2.55
Wi
3.18
5.91
4.64
2.53
1.81
2.81
3.49
1.32
2.79
3.16
Open-source Models 30R)
4.78
6.82
7.06
6.32
4.64
2.55
2.98
3.48
2.98
4.62
DeepSeck-67B
5.22
6.75
5.98
6.66
2.82
3.68
3.75
3.25
4.69
Qwen-72B-Chat
5.72
6.44
6.84
2,85
3.58
Alama2-70B-Chan
3.48
6,08
6.1
6,08
4.65
1,75
2.79
2.5
2.1
3.95
Wi
38

---

Table 25: Subjective evaluation results the dev set of the feedback dimension.
Model
Translation
Chat
QA
Summary
Harm.
MathCoT
MathPoT
CodeRxee
CodeNN
Cloved-source Models
GPT-4
8.6
8.27
8.14
7.64
7.19
Claude
6.56
5.43
6,93
7.17
7.42
4.11
4.59
5.07
4.04
5.7
GPI-3,5-turb
4.67
6.51
6.3
5.61
5.94
4.39
4.36
4.19
3.54
5.06
PaI.M
4.32
4.62
3.16
1.62
Crilique-tuned LLMs (13B-14B
Auto-J-13B
3.81
5.4
5.21
5.6
4.71
3.24
3:47
2.82
2.81
4.12
UttrCM--138
2.37
5.63
5.6
5,66
4,98
3,86
3,3
2.58
2.78
4,09
Open-source Models (6B-7B)
InteraL
5,93
6.39
5.94
5.25
6.28
3.99
4.67
3.28
3.41
5.02
ChatGLM33-6B
4.31
4.49
5.84
4.57
3.19
2.82
2.06
Balchuan2-7B-Chen
3.42
4.37
5.71
5.05
5.19
2.69
2.18
2.14
2.0
3.64
Owen-7B-Chat
2,03
3.9
2.81
2.63
4,24
1.99
1.84
2.44
1.52
2.6
Iaama2-7B--Can
3.76
4.48
2.2
3.53
2.34
2,05
2.22
Mlstral-7B-instrucc--
6.11
6.77
6.16
2.76
3.46
2.25
4,57
Vicuna-7R-v1.3
3.41
3.91
5.5
4.34
4.6
1.8
1.97
1.7
1.74
3.22
DeepSeek-7B
Yi-6B
3.46
3.32
4.61
3.81
3.3
1.77
1.77
1.78
1.09
2.77
Open-source Models (138-208)
InternI 22-20--hhtt
6.2
6.56
6.97
6,36
6.99
5.2
5.59
5.02
4.62
5.98
Qwen-14B-Chat
2.99
Baichuan2-I39-CC
4.29
5.31
4.99
3.41
4,56
2.29
2.2
1.85
1.58
3.39
InternI.M-20B- M-20B-Chht
2.44
2.74
2.59
2.53
1.03
1.27
1.41
1.43
Lluma2-11 1-122-13B-Cutt
4.79
5.14
5.99
4.73
5.96
2.53
2.17
1.33
3,77
4.54
4
6.56
4.03
3.98
1.94
1.96
1.42
2.28
3.41
Vicuma-33B-v1.3
Yi-34B
3.26
4.13
4.67
4.14
4.2
2.67
3.14
2.63
2.67
3.5
Open-source Models (Larger
Qwen-72B-Chat
5.6
5.77
6.5
6.14
6.01
4.19
4.49
4,25
3.65
5.18
Lluma2-70B-CC
Nissral-K77B-imsttuutddd...
5,46
6.56
7.26
6.62
6.67
3.7
4.11
4.33
3.43
5.35
D6epSeck-67B
5.19
6.44
7.64
6.16
4.5
4,56
4.37
3.52
5.36
39

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Table 26: Subjective evaluation results the dev set of the comparison critique dimension.
Model
Translation
Chat
QA
Summary
Harm.
MathCoT
MathPoT
CodeRxee
CodeNN
Cloved-source Models
GPT-4
8.27
8.6
8.74
8.12
7:57
7.76
6.83
Claude
6,33
7.11
7.66
7.22
7.12
4,4
4.47
4.63
3.67
5.85
GPI-3,5-turb
544
6.37
7.5
7.05
5.43
3,4
3.53
3.55
3.5
5.08
PaI.M
4.3
5.63
Crilique-tuned LLMs (13B-I4B
Auto-J-13B
4.22
5.98
7.23
6.6
4.27
3.67
3.62
2.82
Open-source Models (6R-7R)
Interal-M--7B-Chhat
4.87
6.32
6.47
5.98
3.72
3.77
2.22
2.83
ChatGLM3-66
3,05
3.49
5.18
4.35
3.24
2.77
2.45
2.23
3.12
Baichuan2-7B-CC
3.37
3.67
5,07
4.17
2.23
2.57
1.77
2.02
Qwen-7B-Chat
3.98
4.81
5.01
4.32
4,18
2.2
2.48
2.15
2.1
3.47
Llama2-7B-Chan
1.07
4.61
1.8
2.55
2.39
3.22
3.18
1.3
2,48
2.31
1lstral-7B-instrrrc---..2
2.88
5.7
6.42
6.32
2.6
2.88
Vicuma-BB-v1.3
2.42
3.93
4.29
4.91
1,98
2,18
2.13
DeepSeck--B
2.73
4,73
5.82
5.78
3.32
2.1
2,53
2.13
2.2
3.48
Open-sourrce Models (138-201)
InternLM2-208--4
6.52
7.25
6.63
3.75
4.37
3.03
Qwen- 14B-Chat
4.82
4.32
5.74
5.83
4.18
2.92
2.55
2.28
2.53
3.91
Baichman2-1388 ChBahhaa
4.73
Internl M-20B-Chat
3.25
3.72
3.15
1.77
2.72
2.35
2.43
2.78
2.43
2.73
Lluma2- L3B-Chat
3.1
2.9
3.73
2.12
1.76
2.32
1.23
1.47
2.55
2.35
W-
2.85
4.28
4.52
2,82
1.61
3,06
3,86
2.27
2.99
Vicuma-338-v1.3
3.17
5.81
6.48
5.73
3.33
3.03
3.27
2.17
2.35
3,93
Yi-34B
Open-source Models (Larger than 70B)
Qwen-72BB-Coe
3.48
3.15
Llama2770B-Chul
2.88
3.81
4.59
2.22
2.96
2.81
1.47
1.72
2.2
2.74
Mistral-8K77
DeepSeek-67B
5.17
6.09
7.23
6.42
3.85
2.97
3.67
3.58
4.73
40

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Table 27: Subjective evaluation results the test and dev set of the correction critique dimension. Due to the cost limitation, we do not provide the experimental results on these closed-source API- based LLMs: GLM4-no-tool, Erni Baichuan2 Turbo, Qwen-Mart MiniMax-ababs.
Test Summary
Dev
Transtaion
Translation
QA
Summary
Avg.
Closed-souree LLMs
GPT-4
788
7.82
7.65
7.78
7.4
7.69
7.71
8.08
6.82
7.9
7.2
7.54
Gemini-Pro
7.35
Claude
7.52
7.25
7.58
8.22
7.72
7.32
7.62
7.45
7.7
7.75
7.57
GP1I-3.--turb
7.38
7.62
7.58
7.32
7.84
7.55
7.18
7.28
6.95
7.5
7.03
7.19
PaLM
4.58
5.88
6.68
7.43
6.72
5.48
6.26
KrnieBot
7.95
6.92
7,58
Baichuan2 Turbo
7.69
7.3
7.18
7.62
8.45
7.65
Qwen-Max
8.05
8.25
7,95
8.07
8.75
8.21
MiniMax-ababb
Open-source LLMs (GD-7B)
Interal M2-7B-Chat
6.25
6.78
7.21
7.05
8.55
7.17
6.18
6.86
7:23
6.8
7.7
6.95
Mha6G1M3-6B
3.05
5.72
3.75
5.17
5.05
Y-68
4.47
4.88
5.3
4.4
4.07
5.25
4.87
4.69
DeepSeck-78
5.32
5.32
6.48
1212
7.05
6.06
5.32
5.62
6.55
6.2
6.14
Bakchuan2-7B-Chht
5.8
5.38
5.4
5.42
5.38
5.48
5.84
5,03
5.97
5.98
6,08
5.77
5.58
2.31
2
4.78
3.88
Llama2-7B-- -B-Catt
3.78
6.5
6.55
5.86
8.6
6.26
4.9
3.55
5.37
3.98
1.75
4.51
Vicuma-7B-x1.3
3.42
5.42
5.58
7,82
5.61
3.63
5.25
6.27
5.43
Nistral-7R-instruu--11..
Open-source LLMs (23-20B)
Interal M2-200H-hhtt
6.41
7.5
7.6
7.28
8.6
7.48
6.62
7.22
7:78
6.68
7.97
7.25
Qwen-14B-Chat
7.22
6.45
7.08
8.3
1.25
6.91
7.55
7.05
Buichuan2-130 L-h--hhht
Internl M-201B-Chat
3.75
4.55
5.03
4.977
7.72
3.53
4.52
5.08
5.87
5.87
4.97
Llama2-13B-Che
5.45
7.18
7.18
8,75
7.11
4.79
6.6
7.32
5.1
7.36
6.31
Yi-3118
Vicuma-33B--1.22
7.02
WimardLM-13B-v1..
5.31
6.22
6.4
5.89
8.35
6.43
5.39
6.32
6.56
6.14
7.22
6,33
Open-source LLMs 708)
Owen-72B-Chat
Llama2-70B-CL Chat
5.58
7.2
6.85
7.18
8.72
7.11
3.33
5.84
6.72
3.96
7.85
5.54
Mixtral-8x7B--a (enril--x77-isttut----lll
5.18
7,88
7.5
7.5
8.6
7.35
5.07
7.42
7.34
7.68
7.95
7.09
DeepSeck.67B
7.02
6.72
8.36
6.72
7.17
6.92
41

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Table 28: Objective evaluation results the test and dev of the correction dimension. Due to the cost limitation, we do not provide the experimental results following closed-source API-based LLMs on dev set: no-oono-tool, EmiiBott-Pro, Baichuan2 Turbo, Qwen-Max, MiniMax-abab5.
Model
Test
Dev
Avg.
Avg.
Cloved-source LLMs
GPT-4
83.33
83.33
69.67
$0.95
75.61
67.64
Gemini-Pro
Cloude
50
30
66.67
53.33
50
42
68.25
53.66
49.98
GPI:3.5-turbo
42
83.33
76.67
64.00
72
71.43
70.73
62.04
GI.M4-no-tools
73.33
73.33
60.67
ErnieBot Pro
43.75
56.25
83.33
60
60.83
Baichuan2 Turbo
56.67
46.67
47.34
Qwen-Max
55.56
55.45
MiniMax-ahahS5
53.33
43.33
43.67
Open-source LLMs (6B-7B)
Internl.M22-17 M2-77--hhttt
ChatGLM3-6B
36
16
40
30
30.50
30
26
36.51
21.95
28.62
Yi-BB
10
9.5
14.29
12.2
9.12
DeepSeek-7B
Baichuin2-7B--hhut
12.5
16.67
13.54
20.63
19.51
26.04
"wen-7B-Chat
38,00
28,00
30,00
33,33
32.33
34
40
25.4
29.27
32.17
Makenn.M-7l--hhut
10
7.32
7.21
Lama2-78-Clat
1,59
0.90
Vicuna-7B-v1.3
20
24
30
16.67
22.60
20
28
15.87
14.63
19.63
Mistral-7B-instrutt--00
40
36.67
38.17
16.67
37,5
27.27
26.61
Open-source (13-208)
InternLMM--00BChhall
44
38
60
60
50.5
30
52
61.9
56.
50
Owen-14B- 14B-Chat
46.67
33.33
38
42.86
26.83
37.92
Baichumn2-13U4
33.33
Maternl.M-20B-Chhal
10
6.67
18.67
20.63
7.32
21.49
LJama2-1388 13B-Chat
0
8.50
24
D
6.5
19.05
1icuma-31--x1.2
20
30.5
11.11
25
13.64
19.94
W
32
36
23.33
6.67
24.50
32
50
25.4
7.32
28.68
Open-source L.Mss 70R)
Owen-72B-Chat
66.67
54.67
65.08
51.22
$3.08
Llama2-70B-Che
20
0
6.67
6.67
20
0
7.32
8.33
WizardLM-70B--V V1.
10
6.5
Mixtral-&x7Biimstrrees
DeepSeck-67B
36
54
66.67
63.33
55
32
72
63.49
56.1
55.90
M Human Performance in CRITICEVAL
In this section, we provide more details and comparison between LLMs and human performance. Specifically, conduct the human annotation of the subjective tasks on the CRITICEVAL test set, and the overall human-level performance is shown in Table 29 Note that the cohort and corresponding of human critiques does not represent the best possible human performance; instead, they represent the capability of annotators selected for this human performance annotation of the CRITICEVAL test set.
Table 29: Comparison between Human Performance and GPT-4-turbo.
F.
F, Obj.
F,
F,
CR Sub.
CR
GPT-4
7.84
63.54
7.89
57.33
7.69
69.67
Human
5.61
67.69
5.22
60.67
6.63
75.69
can be found that the human-level significantly outperforms GPT-4 on the objective task, while is inferior to GPT-4 the subjective evaluation. Therefore, conduct the Quantitative and Qualitative Analysis to understand the performance gap between humans and GPT-4 in subjective evaluation.
Quantitative Analysis We conduct the fine fine-grained failure modes analysis, and the distribution of each failure mode for feedback, comparison and correction dimensions are shown in Table (30 Table 31 and Table 322 The numbers in following tables indicate the frequencies of error types in GPT-4 and human-written critiques. The detailed description of each failure mode can be found in Section 6.8 As for the feedback and comparison dimensions, the distribution of E1 (missing (insufficient analysis) in human- written critiques is significantly higher than that of GPT-4. In contrast, the distribution of other
issues), B2 (missing suggestions or low-quality suggestions),
42

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error types is significantly lower. As for the correction dimension, human annotators usually do not follow suggestions in the provided feedback (E9) and generate additional errors (E11). Through communicating with the annotators, we notice that the primary cause of this issue is that some tasks require domain-specifie knowledge, and the lack of this knowledge among human annotators results in lower-quality corrections. In summary, the human-written critiques are often less comprehensive than GPT-4, significantly reducing the quality. In contrast, the mistakes in human-written critiques significantly less than that of GPT-4. This phenomenon is consistent with our preliminary study and recent findings [79 [6, further proving the effectiveness and reasonableness leveraging the human-in mamaa--i--the-log -oop pipeline to construct comprehensive and accurate reference critiques.
Table 30: Comparison between Human Performance and GPT-4-turbo in feedback dimension (F.).
E1
E2
E3
E4
E5
E6
Other
GPT-4
17.99
18.71
16.37
15,83
10.07
14.93
6.12
Human
21.18
24.48
11.36
15.27
9.06
12.13
6.52
Table 31: Comparison between Human Performance and GPT-4-turbo in comparison dimension (Fo).
E1
E2
E3
E4
E6
E8
Other
GPT-4
16.67
11.02
11.29
15.59
4.30
6.99
19.35
12.10
2.69
Human
19.71
15.29
7.65
9.51
3.82
4.80
24.90
11.67
2.65
Table 32: Comparison between Human Performance and GPT-4-turbo in correction dimension (C
E1
E2
E3
Other
GPT-4
23.46
43.83
21.60
11.11
Human
29.38
36.88
25.00
8.75
Qualitative Analysis We inspect the human-writter critiques in the subjective evaluation tasks to understand the source of the performance difference. In general, human annotators write fewer comments than LLMs, and the comments are usually general and brief. Besides, many tasks involve domain-specinn knowledge that humans may lack, but GPT-4 excels in (albeit with potential hallucinations).
N Evaluated LLMs
We extensively evaluate widely used open-source and closed-source LLMs of different sizes on CRITICEVAL to understand the current progress this field, including (1) instruction-tun LLMs; (2) critique-tuned LLMs; and (3) reward models. To reproduce evaluation results, the greedy search employed for open-source LLMs,, and the temperature factor set as 0 closed-source L.MSs, iee, decoding randomness is minimum.
The inference procedures of all these evaluated LLMs this paper are conducted in an A800 server with GPU cards, each with 80G CUDA memory. The vLLM [82) and LMDeploy (83) packages are used speed up the inference, and the average inference time cost for each LLM is 1.25 hours.
N.1 Instruction-tuned LLMs
For closed-source LLMs, we test GPT-4, Claude, Gemini-Pro, PaLM, GPT-3.5-turbo, etc. For open-source LLMs, test numerous LLM series including Mistral 44), LLaMA2 (37]. Baichuan2 [84, Qwer [1], InternLM2 (48), WizardLM (85) Vicuna [86). Yi, and DeepSeck [50]. etc.
N.2
Critique-tuned LLMs
Recent works have proven that fine-tuning LLMs on critiques generated by GPT-4 significantly improves LLM's critique ability [1]
30
24
12 Llama-2-13B fine-tuned on GPT-4's critique could
Qwen-11.5 1.5 serie LLMs only evaluated in the meta-bee critique dimension.
43

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Petlomanoc
64
5.4
6.-
cad am
Feetbock
Figure 13: Interpretable analysis of the LLM's critique ability subjective evaluation.
achieve close critique ability to GPT-4, and even outperform much larger model, like Llama-2-70B. In this paper, name the LLMSs fine-tuned critiques dataset Critique-tuned LLMss, and comprchensively evaluate critique-tuned LLMs on our proposed CRITICEVAL: (I) TigerScore [30];
(2) Auto-J-13B [ and (3) UltraCM [12
However, there are two popular critique-tuned LLMs are not evaluated in CRITICEVAL. The reasons listed follows: (1) InstruetScore [24] only be used to evaluate limited tasks, like data2text and commonsense, thus we donot test InstructScore in our work; (2) Prometheus [14] are not evaluated because of its high dependence the criteria question, score rubrics and reference answers, which is not fully covered in our benchmark.
N.3 Reward Models
Moreover, we show CRITICEVAL can also be used to evaluate reward models 87 There are lots of reward models that can be publicly accseed. We only evaluate three representative reward models, and leave the evaluation other reward models in our future work (58] (1) UltraRM-13B [12 (2)
Ziya-7B [2]; (3) SteamSHP [33
Interpretable Analysis of the Quality of Textual Critiques
Beyond the simplified average scores from to 10 in the subjective evaluation of CRITICEVAL, also categorize the textual critiques of each LLM into three quality intervals for more interpretable analysis: (1) Low-quality critiques (1-3); (2) Medium-quality critiques (4-6); (3) High-quality critiques (7-10). The results five representative LLMSs on feedback, correction, and comparion- based feedback dimensions shown in Figure [3 It can be found that GPT-4-turbo exhibits strong critique ability and barely generates low-quality critiques. In contrast, the critiques generated by the Llama-2-7080 2-B----7----C 70B-Chat usually low-quality. Besides, the ratio of low-quality critiques generated by some LLMs, like Llama-2-70B- 2-hh-hhat and Qwen-72B-Chat are very high, indicating that they have lot of for improvement.
P Error Patterns in Responses
In this section, we analyze the details and cases about three kinds of error patterns in responses: (I) obvious error; (2) complex error; (3) subtle error. Specifically, we ask human annotators summarize the common eтror cases after they annotate all the textual critiques in CRITICEVAL, and categorize them into obvious error, complex error, and subtle errors Table 33] should noted that these three етror patterns may have other specific eттоr cases in nine domains. However, it is difficult to exhaust all the error case. Thus, our motivation to list, annotate, and analyze as many as possible to ensure the accuracy of our experimental results.
Likert Score for Responses
Figure demonstrates the discernible performance disparities in responses for each task. Since automatic execution leaks quality information, do not collect the correct responses for the
44

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Table 33: Specific cases in each pattern and each data domain in human annotation. Since harmful content is easy to detect, the complex error very rare in CRITICEVAL.
Domains
Obvious Errors
Complex Errors
Subtle Errors
Ambiguous
Spelling mistakes
Misalignment with input
Inacourate translations of idioms Inappropriate expressions Misalignment with the hackground
Translate
Grammatical Clear misuses of words
translation
Incorrect negative expressions
Inappropriate word meaning
Incorrect assumptions
Flawed deductions
Slight misunderstandings of query Insensitivity to cultural differences
Fail fulfill query Grammatical ervors Obvious contradictions
Inconsistent arguments
Chat
Hallucination
Misalignment of style Missing details
Logical reasoning error
Oversimpliticttion
Oversinplificatoo
Subtle Contextual Misunderstanding Missing details required in question
Comprchension Difficulty
QA
Obvious Commonsense Error
Contradiction
Logical reasening
Imprecise answer Missing details
Dependency etror
Hallocination
Harmlessness
Expheit forms of discrimination
Implicit
Contradiction
Contextual misanderstanding
Minor deviations detail
Summary
Missing key point
Factual error
Information integration errors
Fail to fullfill query
Logical reasoning
Arithmetic
Misunderstanding of problem conditions
Imprecise approximations Ignoring constraints
Math CoT
Data entry erroes
Omission key steps Incorrect assumptions
Formula application errors
Math PoT
Syntux error
Algorithm Implementation error
CodcExec
APILibrrry usage crrors
Improper hundling edge
Time Limit Exceaded
CodeNE
Incorrect input/output format
Overall Objective Performance
Overall Subjective Performance
6.25
6.00
55
5.75
Overall Overall Performance
50-
5.50
5.25
45
5.00
Llama2
Llama2
4.75
40
Intemmm22
Internm22
Qwen
4.50
Qwen
DeepSoek
Seek
35
Mistral
4.25
Mistral
200
400
600
200
400
600
Number of Parumeters in LLM (Billion)
Figure 14: The visualization of scaling law of LLM''s critique ability. Under the same L.M series, the critique ability for LLMs becomes better when the size of the model scale increases.
CodeFxec task. Such variation is instrumental in analyzing the impact of response quality on the feedback.
R Visualization of Relationship between Model Scales and Critique Ability
Following previous works g LLM's critique ability and model scales, which are shown in Figure 14 It can be easily found that the critique ability of all L.M series steadily increase with the number of the parameters (scale) increasing.
we provide diagrams to demonstrate the relationship between
45

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S How Few-shot Examples Affect Performance
In this paper, have studied the few-shot prompting strategy. However, our results demonstrate that few-shot prompting reduces performance across various LLMs. As illustrated in Table 34 when using 1-5 examples in objective feedback evaluations, we observed a significant decline LLMs' performance as the number of examples increased. This intriguing phenomenon may be due to the complexity of the critique task, where few-shot examples might impede the LLM's understanding of the evaluated responses. Consequently, CRITICEVAL currently does not utilize few-shot prompting by default. Given the emerging interest in critique ability research, we look forward to future works investigating advanced inference strategies to improve critique ability of L.MMs.
Table 34: The critique performance (Spearman correlation) of LLMs by few-shot prompting.
Models
No. Few-shot
1
3
4
Llama-3-7B-Char
61.34
58.13
54.25
52.99
53.23
50.11
InternL.M2-20B-CH.
69.86
66.26
64.99
63.32
60.33
61.72
T
Scalability and Cost about CRITICEVAL
In this section, we provide the cost of constructing one task and inferencing one LLM in CRITICEVAL.
T.1
Construction Cost
Collect Evaluated Responses for All Tasks
Open-source LLMs: a GPU server with 8 A100 (80G) cards is used to generate evaluated responses, and the total GPU hours 4.26 hours, approximately 82.88$ (refer the price of Alibaba Cloud).
Closed-source LLMs: the average cost for each LLM 0.89$.
Generate and Revise GPT-4 Critiques The cost of the human annotation is computed under these settings: (1) Four human annotators (3 annotators and one supervisor); (2) 5.69$ hourly wage for each annotator; (3) Average 400 samples in one task. The overall construction cost are shown in Table 335 which is affordable 88].
Table 35: The cost of constructing the critiques.
For Each New Task Generate Critiques (GPT-4)
Cost ($)
Time (hour)
3.09
Human Annotation
303.53
53.34
Overall
306.62
53.34
T.2 Average Computation Cost for One LLM
As shown in Table The overall cost of the test and dev is 13.1949.94=23..1 comparable to the evaluation cost on the AlpacaEval benchmark (5-15S) [19]. These costs are essential for CRITICEVAL, as they guarantee the reliability of critique evaluation. We promise add these details to the Appendix of our revised submission.
U Multilingual Support
The primary goal of CRITICEVAL in the current stage to construct a reliable and comprehensive evaluation for critique ability. We agree that it is essential to study multilingual critiques and intend to broaden CRITICEVAL. to include other languages future work. The following content briefly introduces our preliminary solution how to achieve this goal.
46

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Table 36: The computation of inference LLM in CRITICEVAL.
Dimensions
Cost of Test set ($)
Cost of Dev set ($)
Feedback 4.21
5.09
Correction 2.11
2.67
Comparison 3.62
5.43
Overall 9.94
13.19
Construct Multilingual CRITICEVAL Following the previous work 189] CRITICEVAL could be translated to various languages, especially low-resource languages, with human annotation for revising translation inaccuracies. the most direct way is to translate CRITICEVAL into various languages, with human annotation for revising translation inaccuracies.
Evaluate Multilingual CRITICEVAL While the reliability of objective evaluation could be ensured, the reliability of subjective evaluation is limited by the multilingual capability of the judge model (GPT-4). We recommend back-translating multilingual critiques into English and evaluating them within English CRITICEVAL.
47

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54


Reviews:
[{"review_id": "7h8HfcgxB0", "reviewer": "Reviewer_Hopm", "summary": "This study constructs a comprehensive framework for LLM-based evaluation, encompassing data construction, human/machine annotation, and result analysis. It defines a single evaluation framework that covers various tasks and response types. Although previous studies have evaluated different tasks and response types, they often lacked comprehensive analysis, making it difficult to understand the relationships between various factors during evaluation. This research aims to address this gap. Additionally, it examines the reliability of evaluations based on the types of judge models and target models, as well as the importance of human annotation data.\nThrough extensive experiments and comprehensive analysis, this study demonstrates consistent trends across various tasks and highlights the importance of utilizing human-annotated data in evaluations.", "questions": "1. What is the pattern of score changes when using feedback from other LLMs in Table 5, based on the quality of the feedback from these other LLMs?", "limitations": "Yes, this study clearly mentions its limitations in the conclusion.", "rating": 9, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The proposed framework allows for the evaluation of various tasks and response types.\n2. Through various experiments, the high evaluation capability of closed-source LLMs, regardless of response type and task, is confirmed.\n3. The importance of human-annotated data is evident, showing consistency regardless of the evaluation model.\n4. Additionally, extensive experiments analyze various aspects such as response quality, difficulty, and the capability of reward models as judge models.", "weaknesses": "1. One important metric for evaluation models, the ability to revise its generation with critique, is excluded. As mentioned in the paper, there is active research on improving generation performance through LLM’s self-feedback during the inference stage. It is necessary to evaluate each model’s ability to revise existing outputs when provided with critique.\n2. Although this study addresses the translation task, it excludes the critique ability in other multilingual contexts. The critique ability of LLMs in various languages other than English is crucial for ensuring diversity, and additional data collection on this aspect seems necessary.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We really appreciate your valuable suggestions and insightful questions. Your questions are addressed as follows:\n\n---\n\n**Q1: One important metric for evaluation models, the ability to revise its generation with critique, is excluded ... It is necessary to evaluate each model’s ability to revise existing outputs when provided with critique.**\n\n**A1:** We appreciate the reviewer's insightful comment regarding evaluating the ability to revise generation with critiques. We want to clarify that this capability is a central aspect of our work and has been thoroughly evaluated in our CriticEval benchmark, referred to as the correction ability ($CR$) in our paper. We acknowledge the reviewer's suggestion that \"revision\" is more precise than \"correction\" to describe this feature. We will update the terminology accordingly in the revised version of our paper, replacing \"correction\" with \"revision.\"\n\nTo further clarify the evaluation of the revision ability in our study, we highlight the following points:\n1. Data Collection and Metric for Revision Evaluation:\n* Section 4.3 (line 155) details the process of collecting reference revisions.\n* Sections 5.1 (line 189) and 5.2 (lines 194-196) outline the objective and subjective metrics used to evaluate the model’s ability to revise generations.\n\n2. Evaluating Revision Ability:\nOur evaluation of LLMs’ revision ability encompasses two scenarios: using golden feedback as input and using feedback generated by other LLMs.\n* **Golden Feedback as Input:** Subjective scores are computed by comparing the model’s revisions to human-annotated revisions, and the reliability of this subjective evaluation is substantiated in Section 6.2 (Table 4 and lines 240-246).\n* **Feedback from Other LLMs as Input:** We also conducted experiments to assess how LLMs perform with feedback produced by others, including themselves. As detailed in Section 6.5 (lines 311-326), we evaluated the average performance of the LLMs using three types of feedback: human-annotated, empty, and self-generated. The results indicate that LLMs face challenges in self-improvement, particularly in complex reasoning tasks.\n\nWe hope this clarification addresses the reviewer’s concern and demonstrates the comprehensive nature of our evaluation approach. We are grateful for the opportunity to enhance the terminology and presentation of our findings, which we believe will further clarify the contributions of our work.\n\n---\n\n**Q2: ... it excludes critique ability in other multilingual contexts ... and additional data collection on this aspect seems necessary.**\n\n**A2:** Please refer to Section \"Global Response - Multilingual Support\" for our explanations.\n\nIn summary, our work is the first step to constructing a comprehensive and reliable evaluation. The multilingual feature is not considered because we mainly focus on the critique ability over common tasks in the current stage. But, the cost of extending CriticEval to other languages is affordable.\n\n---\n\n**Q3: What is the pattern of score changes when using feedback from other LLMs in Table 5, based on the quality of the feedback from these other LLMs?**\n\n**A3:** We apologize for any confusion caused by Table 5 and the related content in Section 6.3. Since feedback and revision performance are entangled, it is essential to investigate how the quality of feedback affects the performance of LLM's revision ability.\n\nTo explore this, we prompted the InternLM2-20B-Chat and Llama2-70B-Chat models to revise responses from CriticEval using three types of feedback with varying quality levels. To enhance clarity, we have restructured Table 5 as below: the first table presents results from the objective split of CriticEval, and the second table displays results from the subjective split.\n\n| Revision Model |Source of Feedback|Feedback Quality (1-10)|Objective Revision Performance|\n|-|-|-|-|\n|InternLM2-20B|Llama2-70B|2.24|7.15|\n|InternLM2-20B|InternLM2-20B|7.53|10.33|\n|InternLM2-20B|Human|**8**|**50.5**|\n|Llama2-70B|Llama2-70B|2.24|5.33|\n|Llama2-70B|InternLM2-20B|7.53|12.47|\n|Llama2-70B|Llama2-70B|**8**|**42.43**|\n\n| Revision Model |Source of Feedback|Feedback Quality (1-10)|Subjective Revision Performance|\n|-|-|-|-|\n|InternLM2-20B|Llama2-70B|5.63|5.71|\n|InternLM2-20B|InternLM2-20B|6.85|5.8|\n|InternLM2-20B|Human|**8**|**7.48**|\n|Llama2-70B|Llama2-70B|5.63|5.54|\n|Llama2-70B|InternLM2-20B|6.85|6.32|\n|Llama2-70B|Llama2-70B|**8**|**7.11**|\n\nThese results indicate that as the quality of the feedback increases, both the objective and subjective revision performance improves. This trend demonstrates that higher-quality feedback is associated with more effective revisions.", "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": "SnE088eSPU", "reviewer": "Reviewer_2SKd", "summary": "The paper addresses the need for a comprehensive evaluation of the critique ability of large language models (LLMs) for self-improvement and alignment with human outcomes. Current evaluation methods are critiqued for their limited scope and reliability. The authors propose CRITICEVAL, a benchmark designed to evaluate LLMs across four critique dimensions: feedback, comparison, correction, and meta-feedback, covering nine diverse task scenarios including NLP, alignment, and reasoning tasks. While CRITICEVAL incorporates human-annotated references to enhance reliability, it heavily relies on GPT-4 for evaluations, raising concerns about generalisability across other LLMs. Evaluations of 35 LLMs demonstrate CRITICEVAL's effectiveness but also highlight the inherent difficulties in critiquing complex tasks and the inverse relationship between critique and response quality. Although promising, the reliance on human annotation and the potential biases introduced by using a single LLM for baseline evaluations could limit its broader applicability. The release of datasets and evaluation tools is a positive step towards fostering further research.", "questions": "1. How do you protect against errors in the underlying tasks datasets. The explosion of many task evaluation sets, even with the best intentions of researchers means that we have compounding effects of source errors (e.g. in Translation), how would this affect your work and how would you mitigate against it?\n2. Ultimately how many people are involved and computational cost of such an exercise as this affects replication ability of this study to cover some of the limitations you hilighted?", "limitations": "The limitations are written in Appendix A. They are clear.", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "The work is a very comprehensive study on the critical evaluation problem and provides insight for those who would be in situations where they would want to build better LLM pipelines for use-cases where accuracy is important for fit for use. \n\nThe exploration of 35 different LLM providers more diversity and incorporating human feedback for quality ranking, it allows for wider evaluation than prior benchmarks. \n\nI believe the work is well presented, written and argued. It is a very daunting project to get all the pieces together. It is harder, ironically, to evaluate it just as a paper because of all the moving parts, but the authors evaluations across the LLMs are appreciated.", "weaknesses": "Even with the mention of incorporating Chinese, I believe there should be a serious discussion on how such evaluation pipelines and datasets would function for low-resource languages (Chinese is not one). Many of the large LLMs have multilingual capabilities and their generation of responses are more likely to have higher error rates and as such being able to do critical evaluation in such use-cases is important. \n\nThe computational cost and real cost of setting up the CRITICEVAL pipelines should be spelled out.\n\nIt is not clear if IRB/Ethical clearance was obtained as the checklist response states that IRB *would* be easy to obtain. This is a concern as such I will be referring this for ethics review. It is appreciated that information was provided in Appendix B and G, but whether IRB was obtained or not should have a clear YES or NO statement.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We really appreciate your valuable suggestions and insightful questions. Since some of your questions are similar to those of other reviewers, we will describe them in more detail in **Global Response**. We also provide some summary of these questions under your review comment to make our explanation clear.\n\n---\n\n**Q1: ... it heavily relies on GPT-4 for evaluations ... The potential biases introduced by using a single LLM for evaluations limit broader applicability.**\n\n**A1:** Please refer to **\"Global Response - Overcome Bias of GPT-4 Judge\"** for our explanations. In summary, experimental results in the meta-feedback dimension prove the reliability of GPT-4 as a judge, while other LLMs are much worse. Besides, to further improve the reliability of subjective evaluation, GPT-4 is equipped with our human-annotated critiques, which is a trade-off solution for scalability and reliability of subjective evaluation.\n\n---\n\n**Q2: ... I believe there should be a serious discussion on how such evaluation pipelines and datasets would function for low-resource languages ...**\n\n**A2:** Please refer to Section **\"Global Response - Multilingual Support\"** for our explanations. In summary, CriticEval could be extended to other low-resource languages at an affordable cost using translation and further human labor.\n\n---\n\n**Q3: The computational cost and real cost of setting up the CRITICEVAL pipelines should be spelled out.**\n\n**A3:** Thanks for your valuable suggestion. Please refer to **\"Global Response - Scalability and Cost\"** for more details about the computation and construction cost of CriticEval. In summary, the construction and evaluation cost in CriticEval is comparable to established benchmarks, like AlpacaEval and AlpacaFarm.\n\n---\n\n**Q4: It is not clear if IRB/Ethical clearance was obtained as the checklist response states that IRB would be easy to obtain ...**\n\n**A4:** Thanks for your highly meticulous review. We apologize for any confusion and misunderstanding of the meanings between the human subjects and the crowdsourcing. CriticEval is annotated by crowdsourcing, and no human subjects are studied in our work. Besides, as described in Appendix B, the hourly wage of crowdsourcing is much higher than that of Amazon Mechanical Turk. Thus, our work does not violate the NIPS Code of Ethics. \n\nThank you for bringing this to our attention.\n\n---\n\n**Q5: How do you protect against errors in the underlying tasks datasets ... how would this affect your work and how would you mitigate against it?**\n\n**A5:** We agree that some underlying datasets contain inaccuracies that may lead to compounding effects during evaluation. For example, we have observed some incorrect solutions and reasonings for mathematics and coding questions during our human evaluation process.\n\nIn our work, to mitigate the effects of such errors, human annotators are instructed to examine each question meticulously, the provided golden answers and the evaluated responses and critiques. They are asked to exclude instances where the golden answers or questions are flawed or incorrect, like wrong solutions to mathematics and coding questions.\n\nWe promise to supplement more details and cases in the Appendix. Thank you for raising this critical issue, and we appreciate the opportunity to strengthen our work through your valuable comments.\n\n---\n\n**Q6: Ultimately how many people are involved and computational cost of such an exercise as this affects replication ability of this study to cover some of the limitations you hilighted?**\n\n**A6:** Please refer to Section **\"Global Response - Scalability and Cost\"** for more details about our explanations. Our explanations reveal that the cost of reproducing and extending CriticEval is comparable to established benchmarks like AlpacaEval and AlpacaFarm, ensuring replication ability for the research community.", "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": "WFIXTOPkE2", "reviewer": "Reviewer_kJKF", "summary": "This paper introduces CriticEval, a benchmark designed to comprehensively and reliably evaluate the critique ability of large language models (LLMs). It assesses critique capabilities across four dimensions (feedback, comparison, correction, and meta-feedback) and nine diverse task scenarios, using both scalar-valued and textual critiques for responses of varying quality. The benchmark was constructed using a human-in-the-loop pipeline, with initial critiques generated by GPT-4 and refined by human experts. CriticEval employs both objective metrics and subjective evaluation by GPT-4 with human-annotated reference critiques. Key findings from evaluating 35 LLMs include: GPT-4's high correlation with human judgments when using reference critiques, some open-source LLMs approaching closed-source models in performance, and insights into how critique difficulty varies by task type, response quality, and critique dimension.  CriticEval could be used as a comprehensive and reliable tool for assessing LLM critique capabilities.", "questions": "How did you evaluate the human's performance during the benchmark construction phase?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- CRITICEVAL evaluates critique ability across multiple dimensions (feedback, comparison, correction, meta-feedback) and diverse task scenarios, providing a more holistic assessment than existing benchmarks.\n- The benchmark combines GPT-4 generated critiques with human expert refinement and employs both objective metrics and subjective evaluation with human-annotated reference critiques. This approach ensures a more reliable and accurate evaluation of LLM critique abilities.\n- The paper presents results from evaluating 35 open-source and closed-source LLMs, offering valuable insights into the current state of LLM critique capabilities. It reveals interesting relationships between critique difficulty and factors like task type, response quality, and critique dimension.", "weaknesses": "- The biggest concern of the dataset is that it relies heavily on GPT-4 for initial critique generation and evaluation. This could introduce a bias favoring models such as GPT-4 and models trained on GPT-4 distilled data. The human-in-the-loop process might not fully mitigate this bias, especially if annotators are influenced by GPT-4's initial outputs. A more diverse set of models or purely human-generated critiques for the benchmark could have provided a more neutral evaluation framework. \n- The paper doesn't adequately address the scalability of CRITICEVAL for evaluating future language models. Additionally, the reliance on GPT-4 and human experts for evaluation might make it challenging for other researchers to fully reproduce or extend the benchmark. \n- Insufficient analysis of failure modes: While the paper presents overall performance metrics, it doesn't delve deeply into the specific ways in which models fail at critique tasks. A more detailed error analysis could provide valuable insights into the limitations of current LLMs and guide future research more effectively.\n- Lack of comparison to human performance: The paper doesn't provide a clear comparison between LLM performance and human performance on these critique tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We really appreciate your valuable suggestions and insightful questions. We will address your concerns as follows.\n\n---\n\n**Q1：... relies on GPT-4 for evaluation. This could introduce a bias favoring models such as GPT-4 ...**\n\n**A1:** Please refer to **\"Global Response - Overcome Bias of GPT-4 Judge\"** for more details. In summary, we supply the human-annotated critiques to improve GPT-'s reliability, and experimental results in the meta-feedback dimension have validated its reliability (Section 6.2).\n\n---\n\n**Q2: ... a more diverse set of models or purely human-generated critiques provide a more neutral evaluation framework.**\n\n**A2:** As described in Appendix A.1, human annotators might be influenced by GPT-4' initial critiques. **We wish to emphasize that our decision to employ a human-in-the-loop rather than pure human annotation is motivated by two essential considerations (effectiveness and efficiency)**:\n#### **1. Human-written Critiques are Not Comprehensive (Effectiveness)**\nOur human annotation reveals that annotators might neglect some apparent or severe issues when writing critiques from scratch, consistent with findings in recent studies [1,2]. Neglecting issues usually leads to low-quality critiques. Specifically, experimental results in Table 2 of  **Global Response - Fine-grained Failure Modes** proves that missing issues in critiques lead to low subjective scores.\n\nIn contrast, despite the possibility of generating inaccurate critiques, LLMs like GPT-4 offer more comprehensive and detailed critiques [1,2]. By revising LLM's errors by human experts, the final critiques could be more comprehensive and accurate, leveraging the strengths of both human annotators and LLMs [1,2].\n\nAs detailed in Appendix G.5, our human annotations exhibit significant revisions (25.22%, 34.83%, and 48.37%) on GPT-4's initial critiques, effectively alleviating potential bias and noise.\n#### **2. Annotating Critique Task from Scratch Cost A Lot (Efficiency)**\nWritting critiques from scratch is a significant challenge [3,4]. For instance, Shepherd [4] incurred an annotation cost of 8\\\\$ per sample, leading to over 28,864\\\\$ and 1,350 work hours to annotate the entire CriticEval, which is unbearable for our project. Thus, we have to employ advanced LLMs to generate draft critiques, followed by human annotation.\n\n**GPT-4 is chosen because our preliminary studies indicate that it is the most reliable LLM for producing draft critiques, while other LLMs are much worse (Table 2). Consequently, a diverse set of LLMs introduces more noise in draft critiques, bringing more difficulties to human annotators.**\n\nIn conclusion, the human-in-the-loop pipeline achieves the trade-off between annotation cost and quality. We promise to add these details to the revised paper to emphasize the motivation of using the human-in-the-loop pipeline. Thanks for your detailed review and insightful question.\n\n---\n\n**Q3: The paper doesn't adequately address the scalability of CriticEval ... reliance on GPT-4 and humans make it challenging to reproduce or extend CriticEval**\n\n**A3:** Please refer to Section **\"Global Response - Scalability and Cost\"** for our explanations. Our explanations reveal that the cost of reproducing and extending CriticEval is comparable to established benchmarks like AlpacaEval and AlpacaFarm.\n\n---\n\n**Q4: Insufficient analysis of failure modes ...**\n\n**A4:** Thanks for your very valuable suggestions. We have conducted coarse-grained and fine-grained analyses of failure modes before. Due to space limitations, we only included the coarse-grained analysis in Section 6.6.\n\nTo address your concern, we have supplemented our findings in **\"Global Response - Fine-grained Failure Modes.\"** This supplementary material reveals intriguing phenomena. For example, the most frequent failure modes are missing errors, lacking effective comparison and worse revision than references for feedback, comparison and correction. Besides, inaccurate critiques usually lead to lower subjective scores. The revision that does not follow suggestions in feedback usually leads to the worst performance.\n\nWe really appreciate your insightful suggestions and the opportunity to address them. \n\n---\n\n**Q5: Lack of comparison to human performance ...**\n\n**A5:** As described in Q1-A1, collecting purely human-annotated critiques is challenging. Thus, human performance is not recorded in the current submission.\n\nWe agree that human performance is valuable, and we are urgently working on annotating human-generated results on the test set. All annotators have an undergraduate level of education. Due to the massive workload of human annotation, subjective annotations are ongoing, and the objective scores have been provided below:\n\n||Feedback (Corr.)|Comparison (Acc)|Correction (Pass Rate)|Meta-Feedback (Corr.)|\n|-|-|-|-|-|\n|GPT-4|63.54|57.33|69.67|62.9|\n|Human|**67.69**|**60.67**|**75.69**|**71.36**|\n\nHuman performance slightly outperforms GPT-4 on four critique dimensions in the objective split. We promise to add full human performance results in the revised paper. \n\nThanks for your valuable suggestions.\n\n---\n\n**Q6: How did you evaluate human performance during benchmark construction?**\n\n**A6:** As briefly described in Appendix G.1, for the textual critiques, the supervisor's (authors) review and revise mechanism ensures the quality of human annotation meets our expectations. For the scalar-based critiques, the supervisors conducted a 5% sample inspection. If the error rate exceeds the threshold, annotators are asked to revise their work until the error rate is lower than the threshold. Besides, the inner-agreement among annotators are computed to make sure their judgments are consistent.\n\n---\n### References\n\n[1] LLM Critics Help Catch LLM Bugs (OpenAI)\n\n[2] Self-critiquing models for assisting human evaluators (OpenAI)\n\n[3] AlignBench: Benchmarking Chinese Alignment of Large Language Models \n\n[4] Shepherd: A Critic for Language Model Generation", "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": "sy34vFcSnw", "reviewer": "Reviewer_xDZX", "summary": "This work introduces a benchmark for using LLMs are critics. \n\nThe benchmark covers four settings:\n1. Providing feedback\n2. Correction of a response with/without feedback\n3. Comparison of two responses for a given query\n4. Providing meta-feedback (feedback on feedback)", "questions": "Did you consider structured generation? \n\nBesides positional bias, did you explore any other biases such as bias to length, style or certain words?\n\nDid you consider few-shot performance?", "limitations": "Two minor limitations:\n- As far as I can tell structured generation was not used which has been shown to improve performance\n- As far as I can tell few-shot performance was not considered.", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "Extensive experiments across models and setups.", "weaknesses": "For weakness I would repeat the limitations I list below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We really appreciate your valuable suggestions and insightful questions. We will address your concerns as follows.\n\n---\n\n**Q1: Did you consider structured generation?**\n\n**A1:** We appreciate the reviewer’s attention to the details of our evaluation. Structured generation, such as JSON output, is relatively new but crucial for applications of LLMs, and we have not considered it yet. While the structured generation is not used, our conclusions are still reliable due to the same generation settings of all evaluated LLMs.\n\nThe primary goal of our proposed CriticEval in the current stage is to construct a comprehensive and reliable benchmark for evaluating critique ability. We will supplement discussions about it in the Limitation. Thanks for your valuable suggestions.\n\n---\n\n**Q2: Besides positional bias, did you explore any other biases such as bias to length, style or certain words?**\n\n**A2:** Yes, in addition to positional bias, we have investigated length bias, which is discussed in Section 5.2 (lines 198-200) and Appendix I. Figure 10 (Appendix I) reveals no significant correlation between the count of unique tokens and the Likert scores from GPT-4’s subjective evaluations across three critique dimensions [1,2], likely due to the human-annotated reference critiques used in the prompt. \n\nRegarding other potential biases, such as style and specific word preferences, our human annotations haven't observed substantial influence from these factors.\n\n---\n\n**Q3: Did you consider a few-shot performance?**\n\n**A3:** Yes, we have studied the few-shot prompting strategy. However, our results demonstrate that few-shot prompting reduces performance across various LLMs. For example, when using 1-5 examples in objective feedback evaluations, we observed a significant decline in LLMs' performance as the number of examples increased.\n\n|Spearman Correlation|No Few-shot|1|2|3|4|5|\n|-|-|-|-|-|-|-|\n|**Llama-3-7B-Instruct**|**61.34**|58.13|54.25|52.99|53.23|50.11|\n|**InternLM2-20B-Chat**|**69.86**|66.26|64.99|63.32|60.33|61.72|\n\nThis intriguing phenomenon may be due to the complexity of the critique task, where few-shot examples might impede the LLM’s understanding of the evaluated responses. Consequently, CriticEval currently does not utilize few-shot prompting by default. Given the emerging interest in critique ability research, we look forward to future works investigating advanced inference strategies to improve critique ability of LLMs.\n\n---\n\n### References\n\n[1] AlpacaEval: An Automatic Evaluator of Instruction-following Models\n\n[2] How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources", "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:
# Global Response
We thank all the reviewers for their insightful and valuable comments. Below, we will address some common questions and concerns of reviewers.

---

## **1. Overcome Bias of GPT-4 Judge (kJKF, 2SKd)**
To mitigate bias of GPT-4 as a judge, our work has made two efforts:
### **1.1 In Construction Phase**
CriticEval collects high-quality critiques by human-in-the-loop pipeline, where human annotators review and revise GPT-4's initial critiques. **The human-annotated revisions for GPT-4's initial critiques are significant (25.22%, 34.83%, and 48.37%), as detailed in Appendix G.5.** Besides, the ground-truth answers for challenging tasks are fed to GPT-4 for high-quality draft critiques for GPT-4 (Appendix G.4).

**Furthermore, we introduce the meta-feedback critique dimension to independently validate GPT-4's evaluation reliability as a judge (Section 6.2 and Table 2). It is purely annotated by humans to evaluate critiques generated by various LLMs, including GPT-4, GPT-3.5-turbo, and two CritiqueLLMs, without the bias of GPT-4.** Our results, presented in Tables 2, 3, and 4, show that only GPT-4 with human-annotated critiques achieves a very high correlation with human judgment in meta-feedback, while the others are far behind GPT-4. These results justify GPT-4 as a reliable judge in subjective evaluations and a diverse set of LLMs as judges introduce more noise in draft critiques, bringing more difficulties to human annotators.
### **1.2 In Evaluation Phase**
Human-annotated critiques serve as reference critiques for subjective evaluation, ensuring GPT-4 does not prefer specific LLMs. Our human annotators haven't observed clear bias towards GPT-4 or LLMs fine-tuned on GPT-4's critiques.

Although we have made our best efforts, the bias of GPT-4 may still not be completely eliminated. **We emphasize that our evaluation method is a trade-off solution considering the scalability and reliability of the CriticEval subjective evaluation. We look forward to subsequent work that can address and resolve this issue.**

---

## **2. Scalability and Cost (kJKF, 2SKd)**
We notice that reviewers kJKF and 2SKd inquire about the scalability and the cost of CriticEval. Thus, we provide the cost of constructing one task and inferencing one LLM in CriticEval.
### **2.1 Construction Cost**
The construction cost consists of two parts: 
1. **Collect Evaluated Responses for All Tasks**
* Open-source LLMs: a GPU server with 8 A100 (80G) cards is used to generate evaluated responses, and the total GPU hours are 4.26 hours, approximately 82.88\\$ (refer to the price of Alibaba Cloud).
* Closed-source LLMs: the average cost for each LLM is 0.89\\$.
2. **Generate and Revise GPT-4 Critiques**
|For Each New Task|Cost (\\$)|Time (hour)|
|-|-|-|
|Generate Critiques (GPT-4)|3.09|-|
|Human Annotation|303.53|53.34|
|Overall|306.62|53.34|

The cost of the human annotation is computed under these settings:
* Four human annotators (3 annotators and one supervisor)
* 5.69\\$ hourly wage for each annotator (Appendix G.1)
* Average 400 samples in one task.

In summary, the human annotation cost for one new task is affordable [1].
### **2.2 Average Computational Cost for One LLM**
|Dimension|Cost of Test set ($)|Cost of Dev set ($)|
|-|-|-|
|Feedback|4.21|5.09|
|Correction|2.11|2.67|
|Comparison|3.62|5.43|
|Overall|**9.94**|**13.19**|

The overall cost of the test and dev set is 13.19+9.94=23.13\\$, comparable to the evaluation cost on the AlpacaEval benchmark (5-15\\$) [2]. Note that these costs are essential for CriticEval, as they guarantee the reliability of critique evaluation. We promise to add these details to the Appendix of our revised submission.

---

## **3. Multilingual Support (Hopm, 2SKd)**

The primary goal of CriticEval in the current stage is to construct a reliable and comprehensive evaluation for critique ability. We agree that it is essential to study multilingual critiques and intend to broaden CriticEval to include other languages in future work, as described in Section 7 (lines 375-376). 

Reviewer 2SKd suggests including a serious discussion on how to achieve this goal. The following content briefly introduces our preliminary solution.
### **3.1 Construct Multilingual CriticEval**

Following the previous work [3], CriticEval could be translated to various languages, especially low-resource languages, with human annotation for revising translation inaccuracies.
### **3.2 Evaluate Multilingual CriticEval**
While the reliability of objective evaluation could be ensured, the reliability of subjective evaluation is limited by the multilingual capability of the judge model (GPT-4). We recommend back-translating multilingual critiques into English and evaluating them within English CriticEval.

---

## **4. Fine-grained Failure Modes (kJKF)**

Reviewer kJKF offered valuable suggestions regarding a fine-grained analysis of failure modes. Coarse-grained failure modes have been analyzed in Section 6.6, and the fine-grained analysis is provided in the PDF file. 

The analysis reveals that the most frequent failure modes are missing errors, lacing effective comparison analysis, and worse revision than references for feedback, comparison, and correction dimensions, respectively. Besides, inaccurate critiques usually lead to lower subjective scores, such as missing crucial errors and incorrect analysis content. The revision that does not follow suggestions in feedback usually leads to the worst performance.

---

### References
[1] AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

[2] Alpacaeval: An automatic evaluator of instruction-following models

[3] Language Models Are Multilingual Chain-of-Thought Reasoners

Meta-review: This work presents an analysis of the evaluation capabilities of large language models (LLMs) at four different tasks: (1) pointwise evaluation of generated responses with textual and numerical assessments, (2) response correction, (3) pairwise response comparison; (4) meta-feedback – the evaluation of generated textual evaluations. Human annotations are collected on 9 specific application scenarios to facilitate the evaluation of the 4 evaluation tasks. A comprehensive evaluation is then performed on 35 LLMs using the collected human annotations as gold references with the assistance of GPT-4.  

The reviewers mostly agree on the following strengths and contributions of this work:  
1. The importance of new human annotation collection in facilitating the evaluation of LLMs’ evaluation capabilities on the four tasks studied.  
2. The comprehensiveness of the evaluation performed, covering 35 LLMs, 9 application scenarios, and 4 evaluation tasks.  
3. The analysis performed in this paper is thorough and offers interesting insights.  

The major concerns the reviewers raised include:  
1. The reliance on GPT-4 in the evaluation pipeline, considering GPT-4 may not be a reliable and unbiased evaluator.  
2. Unclarity of the cost of the evaluation process.  
3, Lack of discussion of multilingual evaluation.  
During the rebuttal period, the authors provided responses that clarified some of the above concerns. Specifically, regarding the reliance on GPT-4, this work has already performed a meta-evaluation of GPT-4 regarding performing subjective evaluations in Section 6.2, which provides an assessment of its reliability. However, the highest correlation achieved between GPT-4 and human annotations is around 66.2, which may not be high enough to demonstrate that GPT-4 is a robust evaluator. That said, the use of GPT-4 is largely justified because it can greatly improve the evaluation efficiency.

This work could be improved by the following:  
1. A clearer and more thorough discussion of the limitations and risks of using GPT-4 as the judge in evaluation.  
2. Clear description and analysis of the human annotation quality. One major contribution of this work is the collected human annotation,  
 and it is of great importance that the human annotations are reliable. However, there is a lack of quantitative analysis of the human annotation quality, such as the inter-annotator agreement rate of pointwise scoring, etc. These statistics are important to demonstrate the quality of the collected annotations. Related work such as LLMBar [1] put a great emphasis on this reliability since it determines whether the evaluation performed on the collected annotations is reliable.

[1] Zeng, Zhiyuan, et al. "Evaluating Large Language Models at Evaluating Instruction Following." ICLR
Decision: Accept (poster)