A Closer Look into Using Large Language Models for Automatic Evaluation
Authors: Cheng-Han Chiang
Hung-yi Lee
Venue: EMNLP 2023 Findings (2023)
Keywords: LLM
automatic evaluation
LLM evaluaiton
TL;DR: We compare two widely used LLM evaluation methods and improves the correlation between ChatGPT's rating and ground truth human ratings.

Abstract: Using large language models (LLMs) to evaluate text quality has recently gained popularity.
Some existing prior works explore the idea of using LLMs for evaluation, while they differ in some details of the evaluation process.
In this paper, we analyze *LLM evaluation* and *G-Eval*, and we discuss how those details in the evaluation process change how well the ratings given by LLMs correlate with human ratings.
We find that the auto Chain-of-Thought (CoT) used in G-Eval does not always make G-Eval more aligned with human ratings.
We also show that forcing the LLM to output only a numeric rating, as in G-Eval, is suboptimal.
Last, we reveal that asking the LLM to explain its own ratings consistently improves the correlation between the ChatGPT and human ratings and pushes state-of-the-art (SoTA) correlations on two meta-evaluation datasets.

A Closer Look into Automatic Evaluation Using Large Language Models
Cheng-Han Chiang
Hung-yi Lee National Taiwan University, Taiwan hungyilee@ntu.ed. tw
National Taiwan University, Taiwan dcm1071 40gmai1.
Abstract
evaluation results of humans. Still, LLM evaluation and G-Eval differ in some specific design choices in the evaluation procedure. Since Chiang and Lec (2023) and Liu et al. (2023) use distinct tasks, it is hard to know how the differences between LLM evaluation and G-Eval affect the evaluation results. This makes practitioners in the future hard to deter- mine how to conduct automatic evaluation using LLMs.
Using large language models (LLMs) to evalu- ate text quality has recently gained popularity. Some prior works explore the idea of using LLMs for evaluation, while they differ in some details of the evaluation process. In this pa- per. we analyze LLM evaluation (Chiang and Lee, 2023)' and G-Eval (Liu et al., 2023), and we discuss how those details in the evaluation process change how well the ratings given by LLMs correlate with human ratings. We find that the auto Chain-of-TT Chain-of-Thought (CoT) used in G-Eval does not always make G-Eval more aligned with human ratings. We also show that forcing the LLM to output only numeric rat- ing, as G-Eval, is suboptimal. Last, we re- veal that asking the LLM to explain its ratings consistently improves the correlation between the ChatGPT and human ratings and pushes state-of-the aht (SoTA) correlations two meta-evaluation datasets.
Given that LLM evaluation and G-Eval have al- ready received significant attention shortly after publication, these methods will likely revolution- ize the evaluation in NLP. Therefore, conducting detailed analysis of these approaches is essential and timely. This paper aims to identify the crucial components in LLM evaluation and G-Eval that contribute to stronger correlations with human rat- ings. Based on our analysis, we provide guidelines on how to use LLMs for automatic evaluations. We have the following findings:
1 Introduction
Auto-CoT (proposed by G-Eval) does not ways improve the correlation between LLM and human ratings.
Large language models (LLMs) trained with task instructions and human feedback can follow natu- ral language instructions to complete task (Askell et al., 2021; Sanh et al., 2022; Wei et al., 2022a; Ouyang et al., 2022). Recently, the instruction- following ability of LLMs makes them promising candidates for automatic evaluation (Chiang and Lee, 2023; Liu et al., 2023; Wang et al., 2023; Huang al., 2023). By simply instructing the LLMs how rate and giving the LLMs the sam- ple to be rated, the LLM can follow the instructions and provide a rating of the sample.
Making the LLMs output only single nu- meric rating is suboptimal.
Asking the LLMs to rationalize their own rat- ings significantly improves the correlation be- tween the LLMs' ratings and human ratings.
On two datasets, we improve the best correla- tion that ChatGPT's rating can achieve, and some correlations even exceed prior SoTA cor- relations obtained using the ratings of GPT-4 in Liu et al. (2023).
Chiang and Lee (2023) propose LLM evaluation and Liu et al. (2023) propose G-Eval; both of which use LLMs to evaluate samples by giving the LLM instructions, and they both show that some LLMs can yield evaluation results that aligned to the
2 Experiment Setup
Our paper studies what components in LLM eval- uation and G-Eval make the ratings generated by LLM correlate with human ratings better, and we aim to improve the correlation.
In this paper, the term L.M evaluation is used to refer to the specific method proposed by Chiang and Lee (2023).

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2.1
LLM as an Automatic Evaluation Metric
indicates the evaluation metric better aligns with human ratings. We adopt Pearson r and Kendall's T as they are widely used meta-evaluations (Gra- ham et al., 2015; Bojar et al., 2017; Zhang* et al., 2020). In our paper, all the correlation refers to the correlation coefficient between the ratings of LLM and human ratings. Details on the calcula- tion of correlation coefficients are in Appendix C.
Both LLM evaluation (Chiang and Lee, 2023) and G-Eval (Liu et al., 2023) propose to ask LLMs to rate sample regarding some attributes of the sample (e.g., fluency, grammaticality) using a k- point Likert scale. They give the LLMs (1) de- scriptions of the rating task, (2) the definition and rating criteria of the attribute be rated, (3) the sample to be rated, and (4) sentence that prompts the LLM to give the rating". The LLM outputs a sequence containing the rating. Unless specified, we follow prior works to sample N = 20 sequences from the LLM and average those ratings as the final rating. While the two methods share the core concept, they differ in two details.
We use SummEval (Fabbri et al., 2021) and Topical-Chat (Gopalakrishnan et al., 2019; Mehri and Eskenazi, 2020) as the meta-evaluation datasets, following Liu et al. (2023). SummEval is meta-evaluation dataset for summarization de- rived from the CNN/DailyMail dataset (Hermann et al., 2015). Each summary in SummlEval is rated by humans based on the coherence, consistency, fluency of the summary, and relevance between the summary and the source document. Topical-Chat is dataset that evaluates the quality of a response given the dialogue history and a piece of knowl- edge relating to the dialogue. We follow Zhong et al. (2022) to evaluate the naturalness, coherence, engagingness, and groundedness (whether the re- sponse is grounded on the provided knowledge) of the response. The dataset details are in Appendix E.
Difference 1: Auto Chain-of-Thought The task descriptions and rating criteria in LLM eval- uation and G-Eval are all human- hritenwritten. How- ever, Liu et al. (2023) argue that some evaluated attributes require more than simple definition and evaluation criteria, so they use LLMs to determine the evaluation steps. Specifically, they concatenate the task description, definition, and criteria of the attributes and append a line "Evaluation steps:" prompt the LLM. The LLM then generates an ordered list containing the step-by-step evaluation steps. They dub this process auto chain-of-i tuain-of-thought (CoT). G-Eval uses Human--rittedd task instructions and
2.3 Large Language Models
An LLM used as an evaluation metric should be affordable and accessible to whoever wants to use it. Based on this principle, we use ChatGPT (gpt3. 5-tubboo660)))113 (OpenAI, 2022) for evalu- ation since it has lower cost and improved perfor- mance compared with other GPT-3.5 models. Chat- GPT is also used in LLM evaluation and G-Eval, While Liu et al. (2023) further use GPT-4 (OpenAI, 2023) in their experiments, we cannot use GPT-4 in our experiments since most people, including us, have limited or no access to GPT-4, making it utterly unsuitable as an evaluation metric.
evaluation steps to prompt the LLM to rate the sample.
Difference 2: Prompts for Output At the end of the input to LLMs, G-Eval uses the prompt "[{placeholder)) (score only):" restrict the LLM output only the numeric rating; the placeholder will be replaced by the evaluated at- tributes. In contrast, LLM evaluation uses the fol- lowing question to ask the LLM to assign the rating: "How [(placeholder)) is the sample? (on a scale of 1-k, with 1 being the lowest)". The LLM's output form is not restricted.
In our preliminary experiments, we also try to use the best open LLM (at the time of writing this manuscript) on Open LLM leaderboard, the falcon-40b-instruc model (Almazrouei et al., 2023), but we find it cannot follow the instructions and rate the samples very well. Hence, we exclude open LLMSs in our paper.
2.2 Meta-Evaluating an Evaluation Metric
Given a sample, evaluation metric assigns it rating. To evaluate an evaluation metric, we need a dataset containing human ratings for samples in the dataset. We calculate the correlation coefficient between the ratings obtained by the evaluation met- ric and the human ratings. A higher correlation
Better Usage of LLM for Evaluation
'In our paper, we use different highlight colors to repre- sent different parts of the prompt, as shown in the above text. Additionally, we use cyan to represent the parts generated hy auto Chain-of-1 Tooooffoohhhhe Thought
3.1 Is Auto CoT Always Useful?
Liu et al. (2023) shows that adding the evaluation steps generated by auto CoT improves the correla-

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Ablations
Coherence
Consistency
Fluency
Relevance
Sec.
CoT
Output
r
GPT-4
?
Score only
0.581
0.463
0.575
0.419
0.6
0.457
0.599
0.409
V
0.45
0.359
0.37
0.286
0.319
0.203
0.403
0.327
3.1
Score only
X
0.344
0.248
0.328
0.185
0.361
0.177
0.353
0.248
X
Score only
0.344
0.248
0.328
0.185
0.361
0.177
0.353
0.248
X
Free Text
0.46
0.342
0.476
0.334
0.477
0.273
0.324
0.228
3.2
X Rate-explain
0.557
0.44
0.473
0.337
0.451
0.306
0.509
0.348
X
Analyze-rate
0.635
0.476
0.537
0.34
0.479
0.302
0.444
0.305
Table I: The Pearson's and Kendall's correlation coefficient between LLMs' ratings and human ratings for SummEval. All the results in this table, except the first row, are from ChatGPT. We consider auto CoT score only using ChatGPT proposed G-Eval the baseline of this paper. We boldface the Pearson's 's statistically significantly higher than the baseline (except GPT-4). t: results from Liu et al. (2023). Some numbers are different because we re-calculate the correlations based on the GPT-4 responses Liu et al. (2023) released. f: The results of GPT-4 cannot serve reasonable comparison since find something odd in the prompts Liu et al. (2023) use, which we elaborate in Appendix A.
tion on SummEval when using GPT-4 for evalua- tion. By scrutinizing their results, we find that the correlations when using auto CoT and not using it often differ by less than 0.02. This raises two questions: (1) Is this difference statistically signifi- cant? (2) Does auto CoT yield higher correlations for different LLMs and datasets? To answer these questions, we ChatGPT to rate the samples in SummEval and Topical-Chat using two sets of prompts, one with the evaluation steps generated using auto CoT and one without those evaluation steps. In this experiment, we follow G-Eval and restrict ChatGPT to output only a numeric score. Following Graham and Baldwin (2014), we use William's ' test for significance to see if the Pear- son's of using and not using auto CoT is statisti- cally significantly different. We try follow the prompts used in G-Eval when possible; still, we have to construct some prompts since Liu et al. (2023) only release part of the prompts and some of which are problematic. We list all the prompts and how they are obtained in Appendix F.
carefully reproduce their results ourselves. Back to Table 1, we can see that auto CoT leads to higher correlations for coherence, consistency, and relevance. By William's test, these higher cor- relations reach statistical significance with p-values less than 0.05. However, using auto CoT results in lower Pearson's 's for fluency, and this inferiority in Pearson's 's is also statistically significant.
The results for Topical Chat are illustrated in Table 2. For Topical-Chat, the Pearson's r of us- ing and not using auto CoT are very close for all four attributes except groundedness, with differ- ences less than 0.025, and these differences are not statistically significant. For groundedness, auto CoT even drastically decreases the correlation. In summary, using auto CoT does not yield consis- tent and meaningful improvements compared with not using COT. This should not be surprising since the evaluation steps generated with auto CoT of- ten merely paraphrases the evaluation criterion and instructions given to the LLM.
3.2 Prompt for Outputs
The experiment results for SummEval are shown in the block in blue in Table 1. We also list the best results of G-Eval using GPT-4 from Liu et al. (2023) in the first row of Table only for refer- ence. Comparing our results with GPT-4 is unfair since we use ChatGPT, which is weaker than GPT- 4. A more reasonable baseline for paper is the "auto CoT score only" using ChatGPT on the second row, which is the method proposed by G-Eval and shows the highest correlation that Chat- GPT can achieve in Liu et al. (2023). The numbers here differ from results in Liu et al. (2023) because
In this section, we explore if the difference in how ChatGPT is prompted to output makes it's ratings better aligned with human ratings. We use two sets of prompts that share the same task descrip- tions and evaluation criteria but differ in how they prompt the LLM to generate the output. One uses "score only", in G-Eval. The other replaces the "score only" with "How {(placeholder)) is the sample? (on a scale of 1-k, with being the lowest)", as in LLM evaluation. We call the latter prompts free text since they do not

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Ablations
Naturalness
Coherence
Engagingness
Groundedness
Sec.
CoT
Output
0.393
0.358
0.468
0.391
0.549
0.513
0.311
0.566
3.1
Score only
X
0.408
0.331
0.443
0.404
0.557
0.535
0.358
0.582
Score only
0.408
0.331
0.443
0.404
0.557
0.535
0.358
0.582
3.2
X Free Text 0.464
0.476
0.524
0.426
0.611
0.557
0.563
0.666
X Rate-explain
0.524
0.47
0.477
0.416
0.567
0.524
0.58
0.693
X Analyze-rate 0.573
0.47
0.486
0.416
0.628
0.524
0.725
0.693
Table 2: The Pearson's 's r and Kendall's r correlation coefficient between LLMs' ratings and human ratings for Topical-Chat. All the results this table, except the first row, are from ChatGPT, boldface the Pearson's r statistically significantly higher than auto Coll' score only. We underline the Pearson's r comparable auto Coll + score only.
restrict the output form.
chain-of of-in-of-thought (Kojima et al., 2022). Refer Appendix F.1.1 for the exact prompts we use.
The results for SummEval are shown in the yel- low blocks in Table I, and the results for Topical- Chat are shown in Table 2. We find that allowing ChatGPT to respond to the question freely yields Pearson's s r and Kendall's r much higher than re- stricting the model to output single numeric score for almost all attributes of both datasets. The higher Pearson's 's of free text compared with score only is statistically significant. The only exception is the relevance of SummEval, where free text yields slightly lower correlations.
The results of asking ChatGPT to explain/ana- lyze how they rate the sample are shown in the last two rows in Table 1 and Appendix Table We find that for all attributes of both datasets, rate-explain and anlyze-rate rate both lead to correlations stronger than or at least comparable to the correlation of ask- ing ChatGPT to output only a numeric rating (score only). By asking ChatGPT to explain/analyze, we improve the best correlations that can be achieved by ChatGPT in Liu et al. (2023) (the Auto-CoT + score only). Moreover, when asked to explain/an- alyze when rating, CharGPT's correlation can be better than or comparable the state-of-the- cor- relation coefficients obtained from GPT-4 in Liu et al. (2023) for coherence SummEval and three attributes of Topical-Char Chat.. We hypothesize that some attributes (c.g., coherence for SummEval) are harder for ChatGPT to rate, so the correlations for these attributes show larger improvement when ChatGPT explains how it rates the sample.
Initially, we thought ChatGPT aligns better with human ratings in free text because it can generate natural language explanations to justify their rating, making the ratings more correlated with human observe that the responses of ChatGPT when prompted free text mostly contain a single numeric rating, which is the same behavior when instructed by score only. This means that what the model is allowed to generate is more important than what it really generates.
ratings. However,
The above observations make us curious if the correlations be higher if ChatGPT is instructed justify its ratings. Inspired by chain-of-thouge in Wei et al. (2022b) and Kojima al. (2022) (not the auto CoT G-Eval), we ask ChatGPT to provide their reasoning and rationales on the ratings. Instead of asking ChatGPT to output only a score, we construct two types of prompts that ask ChatGPT to rationalize its decision. The first type of prompt, called analyze-rate, asks ChatGPT to analyze the samples regarding the evaluated criteria first and give the rating. The second type of prompt, called rate-explain, asks ChatGPT to provide the numeric ratings first and explain why it gives such a rating. analyze-rad -att is more like the zero-shot
In rate-explain, the output of ChatGPT contains numeric rating followed by some explanations. As an auto-regressive language model, ChatGPT cannot depend on the explanation when generating the rating due to causal attention. If we stop the generation after ChatGPT generates the ratings, the output of rate-explain will only contain the ratings, just like the output forms in score only. Although the ratings in rate-explain do not depend ChatGPT's rationales for the ratings, the ratings still correlate better with human ratings. compared with the ratings in score only. We think this is because when ChatGPT knows needs to explain the ratings, tends to generate ratings that are easier for to explain, and a rating that is more

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aligned to humans' rating easier for ChatGPT to explain.
temperatures for almost all cases. This shows that rate-explain and analyze-rate are more robust than G-Eval with respect to the sampling temperature.
3.3 Empirical Guidelines
Based on the analysis and results in this section, we provide the following guideline: Always ask ChatGPT to explain/analyze when rating. We do not see rate-explain to be significantly better (or worse) than analyze-rate, so it hard determine which one to use. A valid method is sampling some ratings using rale-explain and sampling some ratings using analyze-nate rate and averaging the ratings from the two prompts as the final rating, Using auto CoT is optional since it does not always lead to higher correlations with human ratings. We also find that using auto CoT does not always improve the correlations when ChatGPT is asked explain; this result is shown in Appendix Table 3.
3.4.2 Changing the Prompts
We check if our guideline holds if change the prompt given to ChatGPT. In this experiment, we changed the prompts to ChatGPT by appending some instructions before the descriptions of the rat- ing task. We tried with two prompts: (1) the HHH prompts and (2) the human annotator prompts. The HHH prompt is designed by Bai et al. (2022) to align the output of LLMs to be more harmless, hon- est, and helpful. The human annotator prompt is inspired by Chiang and Lee (2023), who use a sim- ilar prompt to make the LLM behave as a human annotator. These two prompts will be inserted be- fore the prompt we originally used in our paper. We use these two prompts to inject persona into the LLM. This is inspired by Zeng et al. (2023), which shows that the output of GPT3 can be differ- ent when prompted with different persona. The prompts detailed in Appendix F.3.
3,4 Robustness of the Guidelines
LLMs are notorious for their performance fluctu- ation due to the input prompts, and the sequence generated by LLMs can be different when changing the hyperparameters used in decoding. To verify the validity of our empirical guidelines, we con- duct the following two sets of experiments: (1) we vary the temperature used in sampling the output from ChatGPT, and (2) we vary the prompt given to ChatGPT.
The results are shown in Table 6 and summa- rized as follows: rate-explain and analyze-rate con- sistently outperform the G-eval when using the human annotator prompts and the HHH prompts. This indicates that our guidelines are robust toward different prompts. We also find that the correla- tions of G-Eval significantly drop when adding the human-annotator prompts or HHH prompts. On the other hand, the correlation for rate-explain and analyze-rate do not significantly decrease when adding the prompt. This shows that asking the LLM to explain is more robust to the variation of the prompts.
3.4.1 Varying the Temperature
We check if our guideline holds if we change the temperature T during generation. We compare Pearson's r when using the method proposed in G-Eval (Auto-CoT + score only) with rate-explain and analyze-rate under different temperatures used when generating the output from ChatGPT. We follow Chiang and Lee (2023) and use two temper- atures: 0.7 and 0.3.
prompt and the HHH
4 Conclusion
The results are shown in Appendix Table 5 and summarized as follows: First, when fixing the sam- pling temperature, we find that rate-explain and mate always achieve a higher correlation compared with G-Eval. This supports our guide- line that "asking the LLM to explain/analyze out- performs the method proposed in G-Eval." Next, we observe that the correlation of G-Eval when T 0.3 is much lower than that of T 1.0. This shows that G-Eval is not robust to sampling temperature. Contrarily, we find that the corre- lations obtained by rate-explain and analyze-ratt do not significantly change for different sampling
We study how to better use ChatGPT as an auto- matic evaluation tool by serutinizing LLM evalua- tion and G-Eval. We provide concrete guidelines and show that by using those guidelines, the cor- relations of several evaluated attributes given by ChatGPT, publicly usable model, can be higher than or comparable to the ratings given by GPT-4, highly restricted and pricey model. We also show that the evaluation results based our guidelines improve the best correlation that ChatGPT's rating can achieve. We believe our results and guidelines help future researchers better use LLMs for evalua- tion.

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Limitations
general language assistant as a laboratory for align- ment. arXiv preprint orXiv:2112.00866
There are three main limitations of this paper.
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk. Nelson Elhage. Zac Saatield-Doddk Danny Hernandez, Tristan Hume, Scott Johnston. Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan. 2022. Training a help- ful and harmless assistant with reinforcement learn- ing from human feedback.
1. We only use ChatGPT to conduct the experi- ments in this paper. We explain why we chose ChatGPT in Section 2.3. We believe that us- ing ChatGPT already enough since we show that the correlations obtained by using Chat- GPT are already comparable to or better than the previous SoTA results obtained by GPT-4.
2. We only conduct analysis using two tasks, while we know that NLP has more diverse tasks. We do not guarantee that our observa- tions can generalize to all the other datasets. We recommend the users verify the effective- ness of using LLM to evaluate the tasks of interest.
Ondiej Bojar, Yvette Graham, and Amir Kamran. 2017. Results of the WMT17 metrics shared task. In Pro- ceedings of the Second Conference on Machine Trans- lation, pages 189-513, Copenhagen, Denmark. Asso- ciation for Computational Linguistics.
Cheng- Han Chiang and Hung-yi Lee. 2023. Can large language models be an alternative to human evalua- tions? In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics ( (Vol- ume I: Long Papers), pages 15607-15631, Toronto, Canada. Association for Computational Linguistics.
3. We cannot fairly compare our results with Liu et al. (2023), the previous SoTA results, due to multiple reasons. We explain those reasons in Appendix A.
Alexander R Fabbri, Wojeiech Kryšcińskii, Bryan Me- Cann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021. Summeval: Re-evaluating summariza- tion evaluation. Transactions of the Association for Computational Linguistics, 9:391-409.
Ethics Statement
Our paper follows the ACL Code of Ethics. We do not see particular harmful out- come of paper. The code and datasets for reproducing our experiments can be found at https://gitthb. http://ggttuub.cmoo/223330/ (CCoorr-Lokk----LL--EVVlllltttt
Karthik Gopalakrishnan, Behnam Hedayatnia, Qinlang Chen, Anna Gottardi, Sanjeev Kwatra, Anushree Venkatesh, Raefer Gabriel, and Dilek Hakkani- 2019. Topical-chat: Towards nowledge-groundde open-domain conversations.
Acknowledgements
Yvette Graham and Timothy Baldwin. 2014. Testing for significance of increased correlation with human judgment. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 172-176, Doha, Qatar. Association for Computational Linguistics.
We want to thank the reviews for providing de- tailed feedback and actionable suggestions, which helped strengthen our paper. We also want to thank the senior committee members for monitor- ing the reviewing process. Cheng-Han Chiang is supported by a Ph.D. scholarship program by Delta Electronics.
Yvette Graham, Timothy Baldwin, and Nitika Mathur. 2015. Accurate evaluation of segment-level machine translation metries. In Proceedings of the 2015 Con- ference of the North American Chapter of the Asso- ciation for Computational Linguistics: Human Lan- guage Technologies, pages 1183-1191, Denver, Col- orado. Association for Computational Linguistics.
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A
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As a work highly related to G-Eval, we would re- ally like compare our results with G-Evall. How- ever, we encounter difficulties when comparing our results with those in Liu et al. (2023) for the following reasons.
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey. M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szezechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang. Matteo Manica, Sheng Shen, Zheng Xin Yong. Harshit Pandey, Rachel Bawden, Thomas Wang, Tr- ishala Neeraj. Jos Rozen, Abheesht Sharma, An- drea Santilli, Thibault Fevry, Jason Alan Frics, Ryan Techan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. 2022. Multi- task prompted training enables zero-shot task gener- alization. In International Conference on Learning Representations.
G-Eval proposes to use GPT-4 as the evalu- ation tool, while it is currently a highly re- stricted model, and we only have limited ac- cess to it.
* G-Eval only releases the prompts for Sum- mEval. We need to construct the prompts for Topical-Chat based on the human evaluation instructions released by Mehri and Liskenazi (2020). is possible that the prompts we use for Topical-Chat are different from the prompts used in Liu et al. (2023), making their results incomparable to ours.
Jiaan Wang, Yunlong Liang, Fandong Meng, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, and Jie Zhou. 2023. chatgpt good alg evaluator? a preliminary study, arXiv preprint 23iv:2303304044
The prompts of fluency in SummEval released by Liu et al. (2023) in here is problematic so we need to construct new prompts for fluency. Refer to Appendix F.1 for detailed explana- tions. This makes unable to directly com- pare our results with the results in Liu et al. (2023).
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoe V Le. 2022a. Finetuned language models are zero-shot learners. In International Con- ference on Learning Representations.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022b. Chain of thought prompt- ing elicits reasoning in large language models. In Advances in Newral Information Processing Systems.
We cannot reproduce the numbers on the pa- per of G-Eval even when using their official implementation and the GPT-4 responses they release. This means that the only thing we

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do is calculate the correlation coefficient us- ing the data and code released on the official GitHub of G-Eval, but the numbers are quite different from the results in G-Eval's s paper. Moreover, the results of fluency they provide is the result not using auto CoT, but the results of the other three attributes for SummEival use auto COT. That is why we use question mark for the auto CoT field in Table 1.
Consider a dataset with N source documents. and each source document has M corresponding target documents. We also have the human ratings for N target documents on a specific attribute. While each attribute of each target document is rated by more than one human rater, we average those ratings when calculating the correlation coef- ficient. So the N M ratings are the average ratings from different raters. In the case of SummEval, we have
100 source documents and M 16 summaries generated by 16 summarization mod- cls. There are two different methods for calculating correlation coefficients.
The Table 2
Liu et al. (2023) seems to be wrong. The caption (Spearman's and Kendall's 7) does not match the headers (r and p). This makes us hard to compare their results with ours reliably.
C.0.1 Method Dataset-Level Correlation Coefficient
Supplementary Results for Topical-Chat
In this method, we first obtain the ratings on N M target documents from ChatGPT. We then calcu- late the correlation coefficient between the N M ChatGPT's ratings and the N M average human ratings. In this case, the correlation coefficient is calculated among two N . vectors, meaning that the correlation coefficient is calculated across the entire dataset.
Table is the supplementary results for Topical- Chat that we referred to in the main content. We plan to move Table 2 to the main content using the additional one page in the camera-ready version if the paper is accepted. See how Pearson's and Kendall's are calculated in Appendix C.
C.0.2 Method 2: Document-Level Correlation Coefficient
B.1 Is Auto CoT Useful When ChatGPT Is Asked to Explain?
In this method, for each source document, we ob- tain the ratings of its M target documents using ChatGPT. Next, we calculate the correlation coef- ficient between these M ChatGPT ratings and the corresponding M human ratings. After iterating the above process over all the N source documents, obtain the N correlation coefficients. We av- erage the N correlation coefficients the final correlation coefficient. In this case, the correlation coefficient calculated at the document-level and averaged over the whole dataset.
In Table 3, we show the results when we add the evaluation steps generated by auto CoT when ask ChatGPT when prompting with (rate-explain). We find that on groundedness, using auto CoT is worse. However, for the other three attributes, us- ing auto CoT is better. This again shows that auto CoT is not particularly useful.
C Calculation of Correlation Coefficient
In this paper, we calculate Pearson's and Kendall's 7 between human ratings and ChatGPT's ratings. Whether use Spearman's rank corre- lation Pearson's (linear) correlation to evalu- ate the alignment between human ratings and an automatic evaluation metric is long-standing, but there has been an increasing trend towards Pear- son's 'n's correlation since 2014 (Macháček and Bojar, 2014; Graham and Baldwin, 2014; Zhang* et al., 2020). We use the pearsonr and kendalltau tau in scipy.stats for calculating the correlation coeffi- cients. For each attribute of each sample, the rating of ChatGPT is obtained by 20 samples; we set the decoding temperature to and the top-p in nucleus sampling to 1, following G-Eval (Liu et al., 2023).
C.1 How We Calculate the Correlation Coefficient
In Table and in this paper, we use Method (Subsection C.0.1) to calculate Pearson's corre- lation, following the recommendation in Graham et al. (2015). Calculating the correlation coefficient on the dataset level is also used in LLM evalua- tion (Chiang and Lee, 2023). Calculating single correlation coefficient on the dataset level allows to William's test test whether two Pear- son's r are significantly different.
For Kendall's 7 in Table and 2, we follow most prior works (Zhong et al., 2022: Liu et al., 2023) to

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Ablations
Naturalness
Coherence
Engagingness
Groundedness
Sec.
CoT
Output
X
Score only
0.393
0.358
0.468
0.391
0.549
0.513
0.311
0.566
3.2
/
rate-explain
0.554
0.478
0.512
0.429
0.613
0.566
0.555
0.664
X
rate-explain 0.524
0.47
0.477
0.416
0.567
0.524
0.58
0.693
Table 3: The Pearson's and Kendall's correlation coefficient between LLMs' ratings and human ratings for Topical-Chat. All the results in this table, except the first row, from ChatGPT. We boldface the Pearson's s statistically significantly higher than auto Col + score only. We underline the Pearson's r comparable auto Coll + score only.
calculate Kendall's using Method 2 (document- level, Section C.O.2) to understand if ChatGPT can differentiate the quality difference between differ- ent system outputs for the same source document. In fact, we find that Pearson's 's calculated by Method and Method 2 are highly correlated, In Table 4, we show the result of Topical-Chat while we use Method 2 calculate Pearson's r; Kendall's T is still calculated by Method 2. Comparing the results of Pearson's r in Table and Table 4, one can easily see that when method have signifi- cantly higher Pearson's r in Table 2, it will also have significantly higher Pearson's r. We present the calculated by Method because it makes more sense when calculating statistical significance when the correlation coefficient is calculated the dataset-level (Graham et al., 2015).
We download the source documents, summaries, and human ratings from the GitHub repository of G-Eval (https: tgg. wwtp://wwa//ggeeee/e.eee SummEval was released under MIT License, and our usage for research does violate the dataset's initial intention.
E.2 Topical-Chat
Topical-Chat (Gopalakrishnan et al., 2019) is a hrwwledge-grounder open-domain dialogue dataset. The dataset consists of a dialogue context (history), an interesting fact related to the topic of the conversation, and response. Mehri and Eskenazi (2020) releases high-quality human annotations the quality of responses. They construct the dataset as follows: they first sample 60 dialogues context from Topical-Chat, and for each dialogue context and corresponding fun fact, they use a transformer model to generate four responses using four decoding methods. Each dialogue content has two additional responses: the human response and the ground truth response. Thus, there are total of 360 dialogue-respons, pairs. Those pairs are evaluated based on six attributes, and we follow Zhong et al. (2022) and Liu et (2023) to only use four attributes: naturalness, coherence, engagingness, and groundedness (whether the response is grounded on the provided knowledge). We obtain the human ratings of Topical-Chat from the Github repository of UniEval (Zhong et al., 2022); https://gi thub. ctm http://hsonnnggguuuuue/ htbb//mann/reprrdcceddttttt topical_chat. json.
Results of Changing the Temperature and Prompts
We show the results of varying the temperature used sample the ChatGTT output in Table 5. In the experiments in this section, we only sample N samples from the ChatGPT since we find that G-eval and our proposed guidelines are quite robust to the number of samples when N 2 5.
E Datasets
E.1 SummEval
SummEval (Fabbri et al., 2021) is a dataset for the meta-evaluation of summarization. contains 100 source documents, each with 16 summaries ob- tained from different summarization models. Each of the 1600 summaries is rated by three workers recruited on Amazon Mturk and two experts in summarization. Each summary in SummEval is rated by humans based on the coherence, consis- tency, flunccy of the summary, and relevance be- tween the summary and the source document. Each attribute rated based on 5 5-point Likert scale.
F
Prompts
We list the prompts we use in this section. In the main content of the paper and the following parts, we use different highlight colors to represent dif- ferent parts of the prompt. A prompt is composed

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Ablations
Naturalness
Coherence
Engagingness
Groundedness
Sec.
CoT
Output
GPT-4
V
Score only
0.549
0.594
0.627
0.531
V
0.445
0.358
0.498
0.391
0.579
0.513
0.685
0.566
3.1
Score only
X
0.431
0.331
0.507
0.404
0.631
0.535
0.666
0.582
X
Score only
0.431
0.331
0.507
0.404
0.631
0.535
0.666
0.582
X Free Text
0.572
0.476
0.523
0.426
0.676
0.557
0.747
0.666
3.2
X Rate-explain
0.621
0.512
0.472
0.425
0.61
0.509
0.771
0.663
x Analyze-rate 0.573
0.47
0.486
0.416
0.628
0.524
0.725
0.693
Table 4: The Pearson's and Kendall's correlation coefficient between LLMs' ratings and human ratings for Topical-Chat, Note that in this table, both Pearson's and Kendall's are calculated by Method in Appendix C.0.2. All the results in this table, except the first row, from ChatGPT. The results of GPT-4 are from Liu al. (2023) but should not be compared with our results since the prompts they use may be different from the prompt we use. Still, we can see that for naturalness, engagingness, and grounedness, the results of rate-explain and analyze-rate is better or comparable to GPT-4.
of four parts: (I) the descriptions of the rating task, (2) the definition and rating criteria of the attribute to be rated, (3) the sample to be rated, and (4) sentence used to prompt the LLM to give the rating.
prompt ChatGPT to output. This is likely because ChatGPT tends to stick to the rating criteria when prompted with analyze-rate, and when using the rating criteria different from the criteria that are used to instruct the human raters, the scores generated by ChatGPT deviates more from the human ratings. This highlights the importance of using the same instructions to the LLM as those instructions used the human evaluation, as emphasized in Chiang and Lee (2023).
The prompts for different attributes of the same dataset share the same descriptions of the rating task. Different attributes use different detinition and rating criteria. In G-Eval, the prompts also compose of the evaluation steps generated by auto CoT.
First, show an example prompt for coherence. This prompt corresponds to the score only + auto CoT Table 1.
F.1 Prompts for SummEval
The descriptions of the rating task, the definition and rating criteria. the evalua- tion steps for coherence, consistency, and relevance in SummEval from the prompts released by G-Eval in their GitHub repository Inttps://githuu: 8F54105/prompts// summevall. While G-Eval also releases the prompt they use for fluency, we find something highly problematic in the prompt they use. The prompt for fluency asks the LLM rate fluency on a scale of 1 to
Coherence
You will be given one summary written for a news article. Your task is to rate the summary on one metric.
Please make sure you read and understand these instructions carefully. Please keep this document open while reviewing, and refer to it as needed.
Evaluation Criteria:
Coherence (1-5) the collective quality of all sentences. We align this dimension with the DUC quality question of struc ture and coherence whereby "the summary should be well-structured and well-orga nized. The summary should not just be heap of related information, but should build from sentence to coherent body of information about a topic."
BFf21188660007777009911122226669 prompts//uumvvll/ffuuddeee
txt), while the original rating scale in SummEval is to 5. We also find that the original rating criteria used in G-Eval for fluency differ largely from the rating criteria of fluency used for human evaluation in SummEval. Through our experiment, we find that the misalignment of evaluation criteria and evaluation scale significantly decreases Pearson's r with human ratings when using analyze-rate to
Evaluation Steps: 1. Read the news article carefully and

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Auto-CoT
Output
Coherence
Consistency
Fluency
Relevance
Score only
0.356
0.290
0.261
0.263
Rate-explain
0.548
0.482
0.423
0.487
Analyze-rate
0.589
0.439
0.438
0.319
(a) Temperature T 0.3
Auto-CoT
Output
Coherence
Consistency
Fluency
Relevance
Score only
0.394
0.256
0.288
0.334
X
Rate-explain
0.526
0.468
0.414
0.485
X
Analyze-rate
0.605
0.448
0.441
0.392
(h) Temperature 7' 0.7
Auto-CoT
Output
Coherence
Consistency
Fluency
Relevance
Score only
0.450
0.370
0.319
0.403
Rate exteaeepplain
0.557
0.473
0.452
0.509
Analyze-rate 0.635
0.534
0.479
0.444
(c) Temperature T 1.0 (The result Table 1)
Table 5: Comparing G-Eval (Auto-CoT score only) with rate-explain and analyze-rate at different temperatures. We boldface Pearson's statistically significantly higher than the baseline (the first row in each subtable).
Auto-CoT
Output
Coherence
Consistency
Fluency
Relevance
Score only
0.308
0.248
0.265
0.345
Rate-explain
textbf0.526
0.468
0.414
0.485
Analyze-rate 0.589
0.524
0.459
0.416
(a) Results when prompted with the human evaluator prompts.
Auto-CoT
Output
Coherence
Consistency
Fluency
Relevance
Score only
0.325
0.206
0.281
0.301
X
Rate- Rate-explain
0.596
0.465
0.403
0.478
X
Analyze-rate 0.596
0.493
0.475
0.406
(b) Results when prompted with HHH prompts.
Table 6: Comparing G-Eval (Auto-CoT score only) with rate-explain and analyze-rate when using different prompts. We boldface Pearson's statistically significantly higher than baseline (the first row in each subtable).

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identify the main topic and key points.
Note that the following prompts are the com- plete prompts used the model input; they need to be used with the descriptions of the rating task and the sample to be rated.
Read the summary and compare it the
news article. Check
the summary cov
ers the main topic and key points of the news article, and if it presents them in
a clear and logical order.
Coherence
3. Assign score for coherence on a scale of 1 to 5, where is the lowest and 5 is the highest based the Evaluation Criteria.
Evaluation Criteria:
Coherence (1-5) - the collective quality of all sentences. We align this dimen sion with the DUC quality question of structure and coherence whereby "the summary should be well-structured and well-organized. The summary should not just be a heap of related information, but should build from sentence to a coherent body of information about a topic."
Example:
Source Text: {[Document)}
Summary: [[Summary]]
Evaluation Form (scores ONLY):
Coherence:
F.1.1 Different Output Prompts
For different output prompts, which is the ablation in Section 3.2 and the last block in Table I and 2, we only change the yellow parts (the last part) in the example prompt above. There are four output prompts used in Section 3.2: score only, text, rate-explain, and analyze-rate. The prompts for free text attribute-dependent, and we list them in the Their corresponding output prompts are listed follows:
Evaluation Steps:
1. Read the article carefully and identify the main topic and key points. 2. Read the summary and compare to the news article. Check if the summary covers the main topic and key points of the news article, and if it presents them in clear and logical order.
3. Assign a score for coherence on scale of to 5, where is the lowest and is the highest based the Evaluation Criteria.
Score only
Evaluation Form (scores ONLY):
(Attribute):
Rate-explain
Question:
Evaluation Form (Answer by starting wi with "Rating:" and then give the explanation of the rating on the next line by "Ratio nale:"):
How coherent is the summary? That is, how well do the sentences in the summary fit together? (On scale of 1-5, with 1 being the lowest)
(Attribute):
Analyze-rate
Consistency
Evaluation Form (Answer by starting with "Analysis:" " to analyze the given example regarding the evaluation criteria as con cise as possible, and then give the nu meric rating on the next line by "Rat ing:):
Evaluation Criteria:
Consistency (1-5) the factual alignment between the sunmary and the summarized source. factually consistent summary contains only statements that are en tailed by the source document. Annotators were also asked to penalize summaries that contained hallucinated facts.
(Attribute):
F.12 AttributeDepennden Prompts
The definition and rating criteria of the attribute be rated, the evaluation steps generated by auto CoT, and output prompt for text-free are attribute- dependent, and we list them as follows. We use dif- ferent colors to denote different parts in the prompt.
Evaluation Steps:
1. Read the news article carefully and identify the main facts and details it presents.
2. Read the summary and compare it to the

---

article. Check if the summary contains any factual errors that are not supportes ted by the article.
4. Assign relevance score from 1 to
Question:
3. Assign a score for consistency based on the Evaluation Criteria.
On scale of 1-5, with being the lowest, is the summary relevant to the source document and does the summary only contain the important information of the source document?
Question:
How consistent is the summary wi with the source document in terms of the factual alignment? (On a scale of 1-5, with being the lowest)
F.2 Prompts for Topical-Chat
First, show example prompt for naturalness. This prompt corresponds to the score only + auto CoT Table 2.
Fluency
Evaluation Criteria:
Fluency (1-5): This rating measures the
Naturalness
quality of individual sentences, are they well-written and grammatically cor
You will be given conversation between two individuals. You will then be given one potential response for the next turn in the conversation. The response concerns an interesting fact, which will be provided as well.
rect. Consider the quality of individual sentences.
Evaluation steps:
Read the given summary.
Your task is to rate the responses one metric.
Evaluate the fluency of the summary on a scale of 1-5 based on the criteria provided.
Please make sure you read and understand these instructions carefully. Please keep this document open while reviewing, and refer to it as needed.
3. Provide the rating.
Question:
Based the evaluation criteria, how fluent is the summary? (On scale of 1-5, with being the lowest)
Evaluation Crieteria: Naturalness (1-3) Is the response natu rally written??
score of (bad) means that the response is unnatural.
Relevance
Evaluation Criteria:
A score of (ok) means the response is strange, but not entirely unnatural. A score of 3 (good) means that the response is natural.
Relevance (1-5) selection of important content from the source. The summary should include only important informa tion from the source document. Annotators were instructed to penalize summaries which contained redundancies and excess information.
Evaluation Steps:
1. Read the conversation between the individuals.
2. Read the potential response for the
Evaluation Steps:
next turn in the conversation.
1. Read the summary and the source
3. Evaluate the response based on its
document carefully.
naturalness, using the provided criteria.
2. Compare the summary to the source document and identify the main points of the article.
4. Assign rating score of 1, 2, or 3 based on the evaluation.
Assess how well the summary covers the main points of the article, and how much irrelevant or redundant information it contains.
Example:
Conversation History: {(Document)} Corresponding Fact:

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[[Fact])
Coherence (1-3) Does the response serve as valid continuation of the conversa tion history?
Response:
((Response))
A score of 1 (no) means that the response drastically changes topic on ignores the conversation history.
Evaluation Form (scores ONLY):
Naturalness:
A score of 2 (somewhat) means the response refers to the conversation history in limi ted capacity (e.g., in generic way) and shifts the conversation topic.
F.2.1 Different Output Prompts
For Topical-Chat, we also conduct ablations on different output prompts. Those different output prompts for score only, rate-explain, analyze-rate are the same as those listed in Section F.1.1. We do not list them here to save some space. The exact prompts
A score of 3 (yes) means the response is on topic and strongly acknowledges the conversation history.
can be found in the supplementary data of this paper.
Evaluation Steps:
F.2.2 Attribute DDepbndeet Prompts
1. Read the conversation history.
The definition and rating criteria of the attribute to be rated, the evaluation steps generated by auto CoT, and output prompt for text-free are attributc- dependent, and we list them as follows. Again, the the complete prompts
2. Read the potential response,
3. Evaluate the coherence of the response based on the conversation history.
4. Assign a score of 1, 2, or for coherence.
following prompts used as the model input; they need be used with the descriptions of the rating task and the sample to be rated.
Question:
Does the response serve as valid tinuation of the conversation history? (On scale of 1-3, with meaning the response is invalid and meaning the response is coherent)
Naturalness
Evaluation Crieteria:
Naturalness (1-3) Is the response natu rally written??
A score of (bad) means that the response is unnatural.
Engagingness Evaluation Crieteria:
A score of 2 (ok) means the response is strange, but not entirely unnatural. A score of (good) means that the response is natural.
Engagingness (1-3) Is the response dul 1/interesting?
score of (dull) means that the response is generic and dull.
A score of 2 (somewhat interesting) means the response is somewhat inter esting and could engage you in the conversation (e.g., an opinion, thought) score of 3 (interesting) means the response is very interesting or presents an interesting fact
Evaluation Steps:
1. Read the conversation between the two individuals.
2. Read the potential response for the next turn in the conversation.
3. Evaluate the response based its naturalness, using the provided criteria.
Assign rating score of 1, 2, or based on the evaluation.
Evaluation Steps:
1. Read the conversation, the correspond
ing fact and the response carefully.
Question:
scale of 1-3 for engagingness, according to the criteria above.
2. Rate the response
How natural is the reponse? (On scale of 1-3, with being the lowest)
Coherence
Evaluation Crieteria:
Question:

---

Is the response interesting and engaging?
(On scale of 1-3, with meaning dull and meaning interesting)
Groundedness
Evaluation Crieteria:
Groundedness (0-1) given the fact that this response is conditioned on, deter mine whether this response uses that fact.
score 0 (no) means the response
does not mention or refer to the fact at all
A score of (yes) means the response
uses the fact well
Evaluation Steps:
Read the conversation between the two
i individuals,
2. Identify the fact that is provided
for the potential response.
3. Read the potential response.
4. Determine if the potential response
uses or mentions the fact.
5. Assign score of 0 or 1 for grounded ness based on whether the response uses the fact.
Question:
Given the fact that this response is conditioned on, does the response use the fact? (On scale of 0-1, wi with meaning no and 1 meaning yes)
F. Prompts for Section 3.4.2
HHH prompts You are an AI assistant. The AI tries be helpful, polite, honest, sophisticated, emotionally aware, and wumble--ut--krooLeegablle
The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed.
Human annotator prompts Assume that you are a professional and careful human evaluator. You are recruited and paid to conduct the following task. You need to strictly follow the task instruction and ensure that you are doing the job with high-quality.


Reviews:
[{"review_id": "8tpToGoowS", "reviewer": "Reviewer_fsws", "summary": "", "questions": "A) I would expand the limitations section on potential misuses (perhaps in conjuction with the ethical analysis)\nB) How robust you think the results are given the analysis? What are your strongest conclusions?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the insightful reviews and for considering our research question and experiment to be clear. Addressing your concerns helps us improve our paper. We respond to your review as follows. \n\nResponse to Reasons to Reject\n===\n> Evaluation of CoT seems not robust, given **the part release of prompts**. It's mentioned in the limitations, but I am not sure **how useful are the results in the end**.\n\n- **The part release of prompts**: We release all the prompts we used in Appendix E, instead of releasing partial prompts. \n- **Evaluation of CoT seems not robust**: In our experiments, we sample $N=20$ CoT reasoning paths for each rating attribute and for each sample from ChatGPT and average the ratings. We find that varying $N$ (the number of sampled CoT paths) from $N=5$ to $N=20$ merely changes the conclusion in the paper. In the response to reviewer Efov, we also show the results when we vary the temperature for sampling and prompts when asking ChatGPT to rate the samples. We find that, again, the results merely vary: asking the ChatGPT to explain/analyze achieves the best correlation with human ratings. **This shows that CoT (*Rate-explain* and *Analyze-rate*) are quite robust, so our results are very useful.** Please find the details on the experiments in the rebuttal to reviewer Efov.\n\n> Limited evaluation of results/tasks and details on how to reproduce them. To be fair, this is a general remark for all the prompting papers.\n\n- **Limited details on how to reproduce the results**: All the prompts we use are listed in Appendix E. We also specify the models we use in Section 2.3, and all the details about the datasets, including how to download them, are described in Appendix D. One can easily reproduce our experiments based on the information in our paper. We will release all the source codes and prompts we use on GitHub upon acceptance. \n- **Limited results/tasks**: We conduct our experiments on two types of NLP tasks (summarization and dialogue generation), following G-Eval (Liu et al. 2023). In LLM Evaluation (Chiang and Lee 2023), they also conducted the experiments using two tasks. Considering that our paper is a short paper and the two prior works are long paper, the number of results/tasks are comparable to the two prior works.\n\n> More targeted conclusions are missing, e.g. recommendations for further prompting.\n\n- In Section 3.3 (title: empirical guidelines), we provide concrete guidelines on recommendations for users. We restate the conclusions here: **Always ask ChatGPT to explain/analyze when rating; Auto-CoT is optional**. The other two reviewers acknowledge the contribution of these guidelines.\n\nAnswers to questions:\n===\n>  I would expand the limitations section on potential misuses (perhaps in conjuction with the ethical analysis)\n\n- We will discuss the potential misuses, including the possibility that human raters on Mturk or Prolific might use ChatGPT for ratings. We will also discuss some concerns about ChatGPT making human raters lossing their jobs. \n\n> How robust you think the results are given the analysis? What are your strongest conclusions?\n\n- The results are robust for the tasks we tested. In the response to Reviewer Efov, we also say that our results are robust to the number of responses sampled from the LLM, the temperature used during generation, and the prompts given to the LLM. As a responsible scientific paper, we cannot overclaim that the observation will be general to the tasks we did not test. \nOur paper's strongest and most useful conclusion is that “asking the LLM to explain its ratings consistently improves the correlation between the ChatGPT and human ratings and pushes state-of-the-art (SoTA) correlations on two meta-evaluation datasets.”\n\nSummary\n===\nWe believe the above responses address all the concerns of robustness and reproducibility. We also restate the very useful takeaway in the paper to illustrate the value of our paper. We hope the reviewer can kindly raise the soundness, excitement, and reproducibility score based on responses.", "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": "1) Clear research questions \n2) Clear experimental procedure which is important in the LLM era.", "reasons_to_reject": "1) Evaluation of CoT seems not robust, given the part release of prompts. It's mentioned in the limitations, but I am not sure how useful are the results in the end.\n2) Limited evaluation of results/tasks and details on how to reproduce them. To be fair, this is a general remark for all the prompting papers.\n3) More targeted conclusions are missing, e.g. recommendations for further prompting.", "questions_to_authors": "A) I would expand the limitations section on potential misuses (perhaps in conjuction with the ethical analysis)\nB) How robust you think the results are given the analysis? What are your strongest conclusions?", "paper_topic_and_main_contributions": "This paper deals with the task of exploring text quality using LLMs. Authors use LLM Evaluation and G-eval frameworks and compare this with human ratings. There are some important findings, like e.g. the Auto Chain-of-Thought of Geval does not always seem to correlate best with human ratings and also that reducing outputs to a numeric value seems to not be ideal since LLMs can explain their ratings and that adds to explainabilty.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "XtPDoPZjy2", "reviewer": "Reviewer_Efov", "summary": "", "questions": "- Could \"LLM evaluation\" be called something else? It leads to unnecessary confusion in parts of the text\n", "limitations": "", "rating": 3, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank the reviewer for the detailed reviews and the concerns you raised. Addressing your concerns surely makes our paper much better and more sound. We respond to your review as follows. \n\n\n# Summary of Responses (TL;DR)\n\n1. We conduct further experiments on two datasets by changing the temperature and prompts, and we find that our main guideline (*asking the LLM to explain/analyze outperforms the method proposed in G-Eval (using score only + auto-CoT)*) always holds. We also find that the correlations obtained by asking the LLM to explain/analyze are much more stable when varying the sampling temperatures and prompts (compared with G-Eval). Thus, **our guideline is concretely supported by all the experiments.**\n2. We explain why we only use ChatGPT. We argue that our results are useful since we achieve a much better performance using a highly accessible model (ChatGPT). The NLP community can benefit from our results.\n\nWe believe the above responses and additional experiments largely address the reviewer's concerns. We hope the reviewer can kindly adjust the ratings to help us publish this paper.\n\n# Response to Reasons to Reject\n\n> While this work is relevant, it has been only limited to ChatGPT, making the generalization to other LLMs questionable\n\n- We are grateful that the reviewer considers our work relevant, and we think that only using ChatGPT is not a significant weakness of our paper for two reasons. (1) The goal of the paper is to understand previous evaluations using LLMs, and the two prior works both use ChatGPT. We thus limit our study to ChatGPT. (2) Our goal is to use LLMs to conduct **better** evaluations. Our results show that using ChatGPT, when asking it to explain its rating, can achieve much better alignment with humans and sometimes achieve SoTA alignment with human ratings. This result is significant and valuable as it improves the current state of automatic evaluation. We believe such a result is attractive and usable to the NLP community. \n- We only use ChatGPT because this is the only model that is affordable and open enough for most people to use, compared with PaLM (not publicly availble), Claude (only available in USA and UK), or GPT-4 (do not provide API access at the time of June 2023). We do not use other open-source models since we find them unable to follow the task instructions very well. We explain the choice of LLM in Section 2.3. \n\n> Considering the performance of LLMs are sensitive to prompts, type of LLM model, temperature parameter etc., more empirical evidence is needed to concretely support the guidelines\n\nThank the reviewer for this great question! The main guideline we propose is \"asking the LLM to explain/analyze outperforms the method proposed in G-Eval (using score only + auto-CoT)\". In the following supplementary experiments, we conduct further experiments on SummEval and Topical Chat to verify the robustness of the guideline. **The conclusion from Topical Chat is identical to the conclusion obtained from SummEval, and we are only showing the results of SummEval here for simplicity.**\n\n### **Takeaway**\nThe takeaway from the following experiments is that asking the LLM to explain/analyze still outperforms G-Eval when varying the temperature used in sampling and changing the prompts to ChatGPT. We also find that the performance of G-Eval is very sensitive to the sampling temperature, while the correlations obtained by explain/analyze fluctuate less when varying the temperature. This shows that asking ChatGPT to explain/analyze when rating is more robust than requiring the LLM to output only a number. We will add the following experiments in our appendix and shortly discuss this in the main content in our revision.\n\n\n### **Ablation 1: Different temperature $T$ during generation** \nWe check if our guideline holds if we change the temperature during generation. We compare the Pearson's *r* when using the method proposed in G-Eval (Auto-CoT + score only) with *Rate-explain* and *Analyze-rate* under different temperatures used when generating the output from ChatGPT. We follow Chiang and Lee (2023) and use two temperatures: 0.7 (shown in Rebuttal Table 1) and 0.3 (shown in Rebuttal Table 2). We also recap the results in our paper in Rebuttal Table 3, where the temperature is set to 1.0. The ChatGPT we use during rebuttal is `gpt3.5-turbo-0613`, which is the same model we used in the experiments in our paper.\n\nFirst, when fixing the sampling temperature, we can see that *Rate-explain* and *Analyze-rate* always achieve a higher correlation compared with G-Eval (the first row in Rebuttal Table 1, 2, and 3). This supports our guideline that *\"asking the LLM to explain/analyze outperforms the method proposed in G-Eval\"*.\nNext, we observe that the correlation of G-Eval when $T=0.3$ (the first row in Rebuttal Table 2) is much lower than the correlation of $T=1.0$ (the first row in Rebuttal Table 3). This shows that G-Eval is not robust to sampling temperature. However, by comparing Rebuttal Table 1, 2, and 3, we find that the correlations obtained by *Rate-explain* and *Analyze-rate* do not significantly change for different sampling temperatures for almost all cases. **This shows that *Rate-explain* and *Analyze-rate* is more robust than G-Eval with respect to the sampling temperature.**\n\n\n**Rebuttal Table 1. Comparing G-Eval (Auto-CoT + score only) with Rate-explain and Analyze-rate when the temperature of generation is set to 0.7. We boldface the Pearson’s r statistically significantly higher than the baseline (the first row).**\n\n|Auto-CoT | Output | Temperature |Coherence| Consistency| Fluency | Relevance |\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|v | Score only | 0.7 | 0.394 | 0.256| 0.288 | 0.334 | \n|x |Rate-explain | 0.7 | **0.526** | **0.468**| **0.414** | **0.485** |\n|x |Analyze-rate | 0.7 | **0.605** | **0.448**| **0.441** | **0.392** |\n\n\n**Rebuttal Table 2. Comparing G-Eval (Auto-CoT + score only) with Rate-explain and Analyze-rate when the temperature of generation is set to 0.3. We boldface the Pearson’s r statistically significantly higher than the baseline (the first row).**\n\n|Auto-CoT | Output | Temperature |Coherence| Consistency| Fluency | Relevance|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|v | Score only | 0.3 | 0.356 | 0.290 | 0.261 | 0.263 | \n|x |Rate-explain | 0.3 | **0.548** | **0.482**| **0.423** | **0.487** |\n|x |Analyze-rate | 0.3 | **0.589** | **0.439**| **0.438** | **0.319** |\n\n\n**Rebuttal Table 3. Comparing G-Eval (Auto-CoT + score only) with Rate-explain and Analyze-rate when the temperature of generation is set to 1.0 (The result in our paper). We boldface the Pearson’s r statistically significantly higher than the baseline (the first row).**\n\n|Auto-CoT | Output | Temperature |Coherence| Consistency| Fluency | Relevance|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|v | Score only | 1.0 | 0.450 | 0.370 | 0.319 | 0.403 | \n|x |Rate-explain | 1. 0|  **0.557** | **0.473** | **0.452** | **0.509** | \n|x |Analyze-rate | 1.0 | **0.635** | **0.534** | **0.479** | **0.444** | \n\n---\n\n### **Ablation 2: Different prompts to ChatGPT during generation**\nWe check if our guideline holds if we change the prompt given to ChatGPT. In this experiment, we changed the prompts to ChatGPT by appending some instructions before the descriptions of the rating task. \nWe tried with two prompts: (1) the **HHH prompts**, as detailed below\n```\nYou are an AI assistant. \nThe AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.\nThe assistant is happy to help with almost anything, and will do its best to understand exactly what is needed.\n```\nand (2) **human annotator prompts**, as shown below\n```\nAssume that you are a professional and careful human evaluator. \nYou are recruited and paid to conduct the following task. \nYou need to strictly follow the task instruction and ensure that you are doing the job with high-quality.\n```\nThe HHH prompt is designed by Bai et al. (2022) to align the output of LLMs to be more harmless, honest, and helpful. The human annotator prompt is inspired by Chiang and Lee (2022),  who use a similar prompt to make the LLM behave as a human annotator.\nThese two prompts will be inserted in front of the prompt we originally used in our paper. (Precisely, we insert either the HHH prompt or the human-evaluator prompt before Line 734 on page 9.) We use these two prompts to inject persona into the LLM. This is inspired by Zeng et al. (2022), which shows that the output of GPT3 can be different when prompted with different persona.\nWe use temperature $T=1.0$ in this expeirment. The results are shown in the Rebuttal Table 4 and 5.\n\nWe find that ***Rate-explain*** **and** ***Analyze-rate*** **consistently outperform the G-eval (auto-CoT + score only)** when using the human annotator prompts and the HHH prompts. **The results indicates that our guidelines is robust toward different prompts**. By comparing the results in Rebuttal Table 4 and 5 with the results in Table 1 in the paper, we also find that the correlations of G-Eval (the second row in Table 1) significantly drop when adding the human-annotator prompts or the HHH prompts. On the other hand, the correlation for *Rate-explain* and *Analyze-rate* do not show to such significance decreases when adding the human-annotator prompt and the HHH prompt. **This shows that asking the LLM to explain is more robust to the variation of the prompts.**\n\n**Rebuttal Table 4. Comparing G-Eval (Auto-CoT + score only) with Rate-explain and Analyze-rate when using the human annotator prompt. We boldface the Pearson’s r statistically significantly higher than the baseline (the first row).**\n\n|Auto-CoT | Output | Additional prompt |Coherence| Consistency| Fluency | Relevance|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|v | Score only | human annotator prompt | 0.308 | 0.248 | 0.265 | 0.345 | \n|x |Rate-explain | human annotator prompt| **0.526** | **0.468**| **0.414** | **0.485** |\n|x |Analyze-rate | human annotator prompt | **0.589** | **0.524** | **0.459** | **0.416** |\n\n\n**Rebuttal Table 5. Comparing G-Eval (Auto-CoT + score only) with Rate-explain and Analyze-rate when using the HHH prompt. We boldface the Pearson’s r statistically significantly higher than the baseline (the first row).**\n\n|Auto-CoT | Output | Additional prompt |Coherence| Consistency| Fluency | Relevance|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|v | Score only | HHH prompt | 0.325 | 0.206 | 0.281 | 0.301| \n|x |Rate-explain |HHH prompt| **0.596** | **0.465** | **0.403** | **0.478** |\n|x |Analyze-rate | HHH prompt | **0.596** | **0.493** | **0.475** | **0.406** |\n\n# Response to Questions\n\n\n> Could \"LLM evaluation\" be called something else? It leads to unnecessary confusion in parts of the text\n\n“LLM evaluation” is the name proposed by Chiang and Lee (2023). We will change it into \"*LLM-Eval*\" in our revision for clearance.\n\n# Responses to Presentation Improvements\n\n\n> Why are the lines 83 to 102 (and some other parts) are coloured? Could you remove the highlighting of words in the text?\n\nIn the main content of the paper, we use different highlight colors to represent different parts of the prompt. We use (1) red for the task description, (2) green for the definition and rating criteria, (3) yellow for the sentence used to prompt the LLM to give the rating, and (4) blue for the evaluation steps generated by Auto-CoT. The coloring is supposed to make the readers easier to distinguish different parts in the prompts. We will explicitly explain this in the main content of the paper in the camera-ready version. However, if the reviewer thinks the coloring is unnecessary and confusing, we can remove those colorings.\n\n\nReferences\n===\nBai, Yuntao, et al. \"Training a helpful and harmless assistant with reinforcement learning from human feedback.\" arXiv preprint arXiv:2204.05862 (2022).\n\nZeng, Andy, et al. \"Socratic models: Composing zero-shot multimodal reasoning with language.\" arXiv preprint arXiv:2204.00598 (2022).", "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": "1. The paper studies the differences between two popular LLM-based auto evaluation metrics and systematically studies questions such as  \"should auto-COT be always used?\"\n2. The study recommends practices that can be potentially useful for automatic evaluation\n3. The paper is well presented (except for a few text highlights) and is easy to follow", "reasons_to_reject": "1. While this work is relevant, it has been only limited to ChatGPT, making the generalization to other LLMs questionable\n2. Considering the performance of LLMs are sensitive to prompts, type of LLM model, temperature parameter etc., more empirical evidence is needed to concretely support the guidelines ", "questions_to_authors": "- Could \"LLM evaluation\" be called something else? It leads to unnecessary confusion in parts of the text\n", "paper_topic_and_main_contributions": "The paper compares two recently introduced LLM-based evaluation systems: G-eval and LLM evaluation. It's contributions are as follows:\n1. It is not optimal to always make the LLMs output a single numeric number, but asking the LLMs to reason it's rating improves the correlation with human evaluaion\n2. Auto-Chain of Thought reasoning does not increase the correlation between automatic and human evaluation, \n3. Using ChatGPT as the LLM and by making LLMs rationalize its rating, the paper achieves higher correlation with human metrics on two datasets", "excitement": 3, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Why are the lines 83 to 102 (and some other parts) are coloured? Could you remove the highlighting of words in the text?"}, "extra_text": {}}, {"review_id": "QkjwfpQruF", "reviewer": "Reviewer_f4MA", "summary": "", "questions": "Given a text, did you generate the ratings multiple times? When performing an experiment, multiple annotators are asked to give their ratings, which might also differ as the ratings might be subjective. I was just wondering if it is the same for LLMs? Maybe they generate slightly different ratings each time.\n\nDid you also consider open-source models such as LLaMa? ", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive and thoughtful reviews. We are grateful that the reviewer considers our paper interesting and understands the significance of our work. In the rebuttal to reviewer Efov, we conduct further analysis to support our guidelines better. This definitely makes our paper stronger. We hope the reviewer can help us publish our paper in EMNLP 2023.  We respond to your review as follows. \n\n\n# Response to Reasons to Reject\n\n\n> As has been pointed out in the limitation section, then content of the paper is too big to fit in a short paper. One has to often refer to the appendix while reading it. I believe that the main paper should self-contained, which is not in this case.\n\n\n- We will modify our paper using the additional page in the camera-ready version to address the issue. We will move Table 2 (results for Topical-Chat) to the main content, and we will explain the meaning of the colours in Section 2.1. We will also illustrate some of the full prompts (currently located in Appendix E) to the main content for better understanding. We are willing to make further improvements based on the reviewer's feedback!\n\n\n# Reponse to Questions\n> Given a text, did you generate the ratings multiple times? When performing an experiment, multiple annotators are asked to give their ratings, which might also differ as the ratings might be subjective. I was just wondering if it is the same for LLMs? Maybe they generate slightly different ratings each time.\n\n\n- In our experiment, we follow Liu et al. (2023) to sample 20 responses from ChatGPT. We left this detail in Line 566 in the Appendix. Still, we find that using as few as five sampled responses is enough to achieve high correlations with human ratings. We will add the above discussion in Section 2.3.\n\n\n\n\n> Did you also consider open-source models such as LLaMa?\n\n\n- We did not use open-source models since, at the time of writing the paper, we found that the best instruction-following model on Open LLM Leaderboard (Falcon-40b Instruct, which is better than LLaMA) cannot follow the instructions to perform evaluation very well, so we omit open-source LLMs in our study. The above discussions are included in Section 2.3, and we will also add this to the limitation part of our paper. (Note that LLaMA 2 was unavailable when we submitted this paper to EMNLP 2023.)", "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": "Explores an interesting problem of whether LLMs could be utilized to rate text quality. Rating text quality is an expensive process and involves employing human annotators. If LLMs are able to mimic human evaluators, it would be a much cheaper option.\n\nProvides interesting recommendations regarding the usage of LLMs for text rating.\n\nProposes additional prompt strategies to improve the performance.\n\nExperiments performed on real-world datasets.", "reasons_to_reject": "As has been pointed out in the limitation section, then content of the paper is too big to fit in a short paper. One has to often refer to the appendix while reading it. I believe that the main paper should self-contained, which is not in this case. \n\n", "questions_to_authors": "Given a text, did you generate the ratings multiple times? When performing an experiment, multiple annotators are asked to give their ratings, which might also differ as the ratings might be subjective. I was just wondering if it is the same for LLMs? Maybe they generate slightly different ratings each time.\n\nDid you also consider open-source models such as LLaMa? ", "paper_topic_and_main_contributions": "The paper investigates whether large language models (LLMs) could be used to evaluate the quality of a text. Previous studies have demonstrated that LLMs could be prompted to generate ratings across different aspects of text quality. In this work, the authors perform a deeper analysis of the existing work and provide recommendations. For example, they observe that making LLMs to output only a single numeric rating is suboptimal. The authors also propose additional prompting strategies inspired by chain-of-thought prompting, which further improves the results in terms of the alignment of ratings generated by LLMs to human judgement.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper explores the task of using LLMs to evaluate text quality. In particular, they study how components of LLM evaluation and G-Eval contribute to correlations with human raters. They have some interesting findings, e.g., that asking ChatGPT to explain it's rating improves correlations with human raters.
One main concern about this paper is the use of only ChatGPT. While the authors state why they made this decision in their rebuttal, the framing (especially early in the paper, e.g., the abstract) leads one to believe that their findings are more generalizable. The authors present some new results in the rebuttal, but there is missing information making these results seem rushed and incomplete (e.g., lack of a p-value when stating that something is statistically significant). I am concerned about the amount of content in the paper as well, which was mentioned by multiple reviewers. The reader shouldn't have to flip between the paper and the abstract to find details that are crucial to understanding the paper, and the fact that the authors include a note in the limitations section stating "We cannot include all the experiment results in the main content, and we need to make our language as concise as possible due to the page limit" is telling. The authors have made a lot of promises about what they can add to the final page for a camera-ready, but they have also included a huge amount of new results in their rebuttals, and space seems like it will continue to be an issue.
Decision: Accept-Findings