G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment
Authors: Yang Liu
Dan Iter
Yichong Xu
Shuohang Wang
Ruochen Xu
Chenguang Zhu
Venue: EMNLP 2023 Main (2023)
Keywords: generation evaluation

Abstract: The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. 
Conventional reference-based metrics, such as BLEU and ROUGE, have been shown to have relatively low correlation with human judgments, especially for tasks that require creativity and diversity. 
Recent studies suggest using large language models (LLMs) as reference-free metrics for NLG evaluation, which have the benefit of being applicable to new tasks that lack human references.
However, these LLM-based evaluators still have lower human correspondence than medium-size neural evaluators. 
In this work, we present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs.  We experiment with two generation tasks, text summarization and dialogue generation. 
We show that G-Eval with GPT-4 as the backbone model achieves a Spearman correlation of $0.514$ with human on summarization task, outperforming all previous methods by a large margin.
We also propose analysis on the behavior of LLM-based evaluators, and highlight the potential concern of LLM-based evaluators having a bias towards the LLM-generated texts.

G-EVAL: NLG Evaluation using GPT-4 with Better Human Alignment
Yang Liu
Dan Iter
Yichong Xu
Shuohang Wang
Ruochen Xu
Chenguang Zhu
Microsoft Azure Al
Abstract
Moreover, these metrics require associated refer- ence output, which is costly to collect for new tasks.
The quality of texts generated by natural lan- guage generation (NLG) systems is hard to measure automatically. Conventional reference- based metries, such as BLEU and ROUGE, have been shown to have relatively low cor- relation with human judgments, especially for tasks that require creativity and diversity. Re- cent studies suggest using large language mod- els (LLMs) as reference-free free metrics NLG evaluation, which have the benefit of being ap- plicable to new tasks that lack human refer- ences. However, these L.LM- evaluators still have lower human correspondence than medium-sizz neural evaluators. In this work, we present G-EvAL, framework of using large language models with chain-of-thoughte (CoT) and form-filling paradigm, to assess the quality of NLG outputs. We experiment with two generation tasks, text summarization and dialogue generation. We show that G-EvAL with GPT-4 as the backhone model achieves Spearman correlation 0.514 with human on summarization task, outperforming all previous methods by large margin. We also propose analysis on the behavior of M.L--b eval- uators, and highlight the potential concern of L.LM-based evaluators having a bias towards the Ligeeeeeeete texts."
Recent studies propose directly using LLMs reference-free NLG evaluators (Fu et al., 2023; Wang et al., 2023a). The idea to use the LLMs to score the candidate output based on its generation probability without any reference target, under the assumption that the LLMs have learned to assign higher probabilities to high-quality and fluent texts. Meanwhile, it is becoming popular to more powerful LLMs like GPT-4 evaluate smaller student models, like in Alpaca (Taori et al., 2023) and Vicuna (Zheng et al., 2023). However, the validity and reliability of using LLMs as NLG eval- uators have not been systematically investigated. In addition, meta-evaluations show that these LLM- based evaluators still have lower human correspon- dence than medium-size neural evaluators (Zhong et al., 2022). Thus, there need for a more ef- fective and reliable framework for using LLMs for NLG evaluation.
In this paper, we propose G-EVAL,, a framework of using LLMs with chain-of -f-thouhhsh (CoT) (Wei et al., 2022) to evaluate the quality of generated texts in a form-filling paradigm. By only feeding the Task Introduction and the Evaluation Criteria as a prompt, we ask LLMs to generate CoT of detailed Evaluation Steps. Then along with the generated CoT evaluate the NLG outputs. The evaluator output is formatted as a form. Moreover, the probabilities of the output rating tokens can be used to refine the final met- ric. We conduct extensive experiments on three meta- netaa-vvaluatin benchmarks of two NLG tasks: text summarization and dialogue generation. The results show that G-EvAL can outperform existing NLG evaluators by large margin in terms of corre- lation with human evaluations. Finally, we conduct analysis on the behavior of L.MM- L.L.-based evaluators, and highlight the potential issue of L.M--base evaluator having bias towards the LLM-generated
1 Introduction
the prompt
Evaluating the quality of natural language genera- tion systems is a challenging problem even when large language models can generate high-quality and diverse texts that are often indistinguishable from human-written texts (Ouyang et al., 2022). Traditional automatic metrics, such as BLEU (Pap- ineni et al., 2002), ROUGE (Lin, 2004), and ME- TEOR (Banerjee and Lavie, 2005), are widely used for NLG evaluation, but they have been shown to have relatively low correlation with human judg- ments, especially for open-ended generation tasks.

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texts.
The prompt should also contain customized eval- uation criteria for different NLG tasks and, such coherence, conciseness, or grammar. For example, for evaluating coherence text summarization, we add the following content to the prompt:
To summarize, our main contributions and find- ings in this paper are:
1. G-EvAL generally outperforms reference- based and reference-free baseline metrics in terms of correlation with human quality judg- ments, especially for open-ended and creative NLG lasks, such dialogue response genera- tion.
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 well-organized. The summary should not just be heap of related information, but should build from sentence sentence coherent body of information about a topic." "
2. We propose to use automatic chain-or Shain-of-lue of thought to improve the performance of LLM-based evaluators by providing more context and guidance.
3. We propose to re-weight the discrete scores by their respective token probabilities to provide more fine- fine-grained continuous score for G- EVAL.
Auto Chain-of- Thouhhss for NLG Evaluation The chain-of-thoughh (CoT) is a sequence of in- termediate representations that are generated by the LLM during the text generation process. For evaluation tasks, some criteria need a more detailed evaluation instruction beyond the simple definition, and it is time-consuming to manually design such evaluation steps for each task. We find that LLM can generate such evaluation steps by itself. The CoT can provide more context and guidance for the LLM to evaluate the generated text, and can also help to explain the evaluation process and results. For example. for evaluating coherence in text sum- marization, we add a line of "Evaluation Steps:" to the prompt and let LLM to generate the following CoT automatically:
4. We conduct an analysis of the potential is- sue that LLM-based metrics have prefer- ence of LLM-generated texts over human- written texts, which may lead to the self- reinforcement of LLMs if LLM-based metrics are used as the reward signal for improving themselves.
2 Method
G-EvAL is a prompt-based evaluator with three main components: 1) a prompt that contains the def- inition of the evaluation task and the desired evalu- ation criteria, 2) chain-of-thoughtt (CoT) that is set of intermediate instructions generated by the LLM describing the detailed evaluation steps, and 3) scoring function that calls LLM and calculates the score based on the probabilities of the return tokens.
1. Read the news article carefully and identify the main topic and key points.
2. Read the summary and compare it to the news article. Check if the summary covers the main topic and key points of the article, and if it presents them clear and logical order.
Prompt for NLG Evaluation The prompt is natural language instruction that defines the evalu- ation task and the desired evaluation criteria. For example, for text summarization, the prompt can be:
3. Assign a score for coherence scale of 1 to 5, where I is the lowest and 5 is the highest based on the Evaluation Criteria.
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 under- stand these instructions carefully. Please keep this document open while reviewing, and refer to it as needed.
Scoring Funetion The scoring function calls the LLM with the designed prompt, auto CoT, the input context and the target text that needs to be evalu- ated. Unlike GPTScore (Fu et al., 2023) which uses

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User Input
Input Context
Task Introduction
Paul Merson has restorted his row with Andros Townsend after Tottenham midfielder was brought on with only seven minutes remoining in his team 's 0-0 drow with Burnley
Article:
You will be given one summary written for a news the summory one
metric
Input Target
Evaluation Criteria
Summary: Poul merson was brought on with only seven minutes remaining with burniey
drow
Coherence [2-5)- the collective quality of ail sentences. We align this dimension with the DUC quality question structure and coherence
Evaluation Form (scores ONLY):
Coherence:
Evaluation Steps
1. Read the news orticle carefully and identify the main topic key points.
Auto CoT
2. Read the summary and compore it to the news orticle. Check the summary covers the main topic and key points of the news article, and it presents them in clear and logical order.
G-EVAL
coherence 5, where is the lowest and the highest based the Evoluation Criteria.
Assign
scale
Weighted Summed Score: 2.59
Figure I: The overall framework of G-EVAL. We first input Task Introduction and Evaluation Criteria to the LLM, and ask generate CoT of detailed Evaluation Steps. Then we use the prompt along with the generated CoT evaluate the NLG outputs in form-filling paradigm. Finally, we use the prohability summation of the output scores as the final score.
the conditional probability of generating the tar- get text evaluation metric, G-EvAL directly performs the evaluation task with form-filling paradigm. This provides a more flexible way to evaluate the text as the model can behave directly based on the evaluation criteria and steps. For ex- ample, for evaluating coherence in text summariza- tion, we concatenate the prompt, the CoT, the news article, and the summary, and then call the LLM output a score from to 5 for each evaluation aspect, based on the defined criteria.
probabilities of output tokens from LLMs to nor- malize the scores and take their weighted summa- tion as the final results. Formally, given scores (like from 1 to predefined in the prompt
of
{#), 82, -.., }, the probability of each score p( (;) is calculated by the LLM, and the final score is:
SCOTE p(s:) x
(1)
This method obtains more fine- gra-grained, continu- ous scores that better reflect the quality and diver- sity of the generated texts.
However, we notice this direct scoring function has two issues:
1. For some evaluation tasks, one digit usually dominates the distribution of the scores, such as 3 for a scale. This may lead to the low variance of the scores and the low correlation with human judgments.
Experiments
Following Zhong et al. (2022), we meta-evaluate our evaluator on three benchmarks, SummEval, Topical-Chat and QAGS, of two NLG tasks, sum- marization and dialogue response generation.
2. LLMs usually only output integer scores, even when the prompt explicitly requests decimal values. This leads to many ties in evaluation scores which do not capture the subtle differ- ence between generated texts.
3.1 Implementation Details
We use OpenAl's GPT family as our LLMs, includ- ing GPT-3.5 (text-davinci-003) and GPT-4. For GPT-3.5, we set decoding temperature to to in- crease the model's determinism. For GPT-4, as it
To address these issues, we propose using the

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Coherence
Consistency p
Fluency p
Relevance
AVG
Metrics
p
7
p
P
ROUGE-1
0.167
0.126
0.160
0.130
0.115
0.094
0.326
0.252
0.192
0.150
ROUGE-2
0.184
0.139
0.187
0.155
0.159
0.128
0.290
0.219
0.205
0.161
ROUGE-L
0.128
0.099
0.115
0.092
0.105
0.084
0.311
0.237
0.165
0.128
BERTScore
0.284
0.211
0.110
0.090
0.193
0.158
0.312
0.243
0.225
0.175
MOVERSscore
0.159
0.118
0.157
0.127
0.129
0.105
0.318
0.244
0.191
0.148
BARTScore
0.448
0.342
0.382
0.315
0.356
0.292
0.356
0.273
0.385
0.305
UniEval
0.575
0.442
0.446
0.371
0.449
0.371
0.426
0.325
0.474
0.377
GPTScore
0.434
0.449
0.403
0.381
0.417
G-EVAL-3.5
0.440
0.335
0.386
0.318
0.424
0.347
0.385
0.293
0.401
0.320
Probs
0.359
0.313
0.361
0.344
0.339
0.323
0.327
0.288
0.346
0.317
G-EVAL-4
0.582
0.457
0.507
0.425
0.506
0.455
0.547
0.433
0.514
0.418
Probs
0,560
0.472
0.501
0.459
0.505
0.473
0.511
0.444
0.502
0.446
CoT 0.564
0.454
0.493
0.413
0.483
0.431
0.538
0.427
0.500
0.407
-Description 0.513
0.424
0.421
0.344
0.447
0.373
0.479
0.388
0.479
0.377
Table Summary-level Spearman (p) and Kendall- Tau (r) correlations of different metries on SummEval bench- mark. G-EVAL without probabilities (italicized) should not be considered a fair comparison to other metries т, as it leads to many ties in the scores. This results a higher Kendall- correlation, but does not fairly reflect the true evaluation ability. More details are in Section 4.
does not support the output of token probabilities. we set 'n = 20, temperature = 1, top_poppp- p = 1' to sample 20 times to estimate the token probabilities. We use G-EVAL-4 to indicate G- E-ELLL with GPT-4 the backbone model, and G-EVAL-3.5 to indi- cate G-EVAL with GPT-3.5 as the backbone model. Example prompts for each task are provided in the Appendix.
task. It aims to measure the consistency dimension of summaries by asking and answering questions. It is collected from two different news summarization datasets CNN//ailyMail and XSum.
3.3 Baselines
We evaluate G-EvAL against various evaluators that achieved state-off-th-aat performance.
3.2 Benchmarks
BERTScore (Zhang al., 2019) measures the similarity between two texts based on the contextu- alized embedding from BERT (Devlin et al., 2019). MoverScore (Zhao et al., 2019) improves BERTScore by adding soft alignments and new aggregation methods to obtain more robust simi- larity measure.
We adopt three meta-evaluation benchmarks to measure the correlation between G-EvAL and human judgments.
SummEval (Fabbri et al., 2021) is bench- mark that compares different evaluation methods for summarization. It gives human ratings for four aspects of each summary: fluency, coherence, consistency and relevance. is built on the CNN//ailyMaii dataset (Hermann et al., 2015)
BARTScore (Yuan et al., 2021) is unified eval- uator which evaluate with the average likelihood of the pretrained encoder-decoder model, BART (Lewis et al., 2020). It can predict different scores depending on the formats of source and target. FactCC and QAGS (Kryšciński et al., 2020; Wang et al., 2020) are two evaluators that measure the factual consistency of generated summaries. FactCC is BERT-based classifier that predicts whether a summary consistent with the source document. QAGS is a question-answering based evaluator that generates questions from the sum- mary and checks if the answers can be found in the source document.
Topical-Chat (Mehri and Eskenazi, 2020) is testbed for meta-evaluating different evaluators on dialogue response generation systems that use knowledge. We follow (Zhong et al., 2022) to its human ratings on four aspects: naturalness, coherence, engagingness and groundedness.
QAGS (Wang et al., 2020) a benchmark for evaluating hallucinations in the summarization

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Naturalness
Coherence
Engagingness
Groundedness
AVG
Metrics
P
p
P
p
r
p
ROUGE-L
0.176
0.146
0.193
0.203
0.295
0.300
0.310
0.327
0.243
0.244
BLEU-4
0.180
0.175
0.131
0.235
0.232
0.316
0.213
0.310
0.189
0.259
METEOR
0.212
0.191
0.250
0.302
0.367
0,439
0.333
0.391
0.290
0.331
BERTScore
0.226
0.209
0.214
0.233
0.317
0.335
0.291
0.317
0.262
0.273
USR
0.337
0.325
0.416
0.377
0,456
0,465
0.222
0.447
0.358
0.403
UniEval
0.455
0.330
0.602
0.455
0.573
0.430
0.577
0.453
0.552
0.417
G-EVAL-3.5
0.532
0.539
0.519
0.544
0.660
0.691
0.586
0.567
0.574
0.585
G-EVAL-4
0.549
0.565
0.594
0.605
0.627
0.631
0.531
0.551
0.575
0.588
Table 2: Turn-level Spearman (p) and Kendall- Tau (r) correlations of different metrics on Topical- benchmark.
USR (Mchri and Eskenazi, 2020) is evaluator that assesses dialogue response generation from different perspectives. It has several versions that assign different scores to each target response.
In the last part of Table which corresponds to GPT-based evaluators, GPTScore also uses GPTs for evaluating summarization texts, but relies on GPT's conditional probabilities of the given tar- get. G-EvAL substantially surpasses all previous state-of-the-aae evaluators on the SummEval bench- mark. G-EvAL-4 achieved much higher human correspondence compared with G-EVAL-3.5 on both Spearman and Kendall-Tau correlation, which indicates that the larger model size of GPT-4 is beneficial for summarization evaluation. G-EvAL also outperforms GPTScore on several dimension, demonstrating the effectiveness of the simple form- filling paradigm.
UniEval (Zhong et al., 2022) is a unified evalua- tor that can evaluate different aspects of text gen- eration QA tasks. It uses pretrained T5 model (Raffel al., 2020) encode the evaluation task, source and target texts as questions and answers, and then computes the QA score as the evaluation score. It can also handle different evaluation tasks by changing the question format.
GPTScore (Fu al., 2023) is framework that evaluates texts with generative pre-training models like GPT-3. assumes that a generative pre-training model will assign a higher probability high-quality generated text following a given in- struction and context. Unlike G-EVAL, GPTScore formulates the evaluation task as a conditional gen- eration problem instead of a form-filling problem. We report the score of GPTScore with GPT3-text- davinci-003 the LLM, which also usually re- ferred as GPT-3.5.
3.5 Results for Dialogue Generation
We use the Topical-chat benchmark from Mehri and Eskenazi (2020) to measure how well differ- evaluators agree with human ratings quality of dialogue responses. We calculate the Pearson and Spearman correlation for each turn of the dialogue. Table 2 shows that similarity-basee metrics have good agreement with humans on how engaging and grounded the responses are, but not on the other aspects. With respect to the learning- based evaluators, before G-EVAL, UniEval predicts scores that are most consistent with human judg- ments across all aspects.
the
3.4 Results for Summarization
We adopt the same approach as Zhong et al. (2022) evaluate different summarization metrics using summary-level Spearman and Kendall-Tau corre- lation. The first part of Table shows the results of metrics that compare the semantic similarity between the model output and the reference text. These metrics perform poorly most dimensions. The second part shows the results of metrics that neural networks to learn from human ratings of summary quality. These metrics have much higher metrics, sug-
As shown in the last part, G-EvAL also substan- tially surpasses all previous state-of-the-art eval- uator on the Topical-Chie -hat benchmark. Notably, the G-EVAL-3.5 can achieve similar results with G- G-EvAL-4. This indicates that this benchmark is relatively easy for the G-EvAL model.
3.6 Results on Hallucinations
correlations than the similarity- gesting that they are more reliable for summariza- tion evaluation.
Advanced NLG models often produce text that does not match the context input (Cao et al., 2018). 8), and

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QAGS-CNN
QAGS-XSUM P
Average
Metrics
r
p
r
p
ROUGE-2
0.459
0.418
0.333
0.097
0.083
0.068
0.278
0.250
0.200
ROUGE-L
0.357
0.324
0.254
0.024
-0.011
-0.009
0.190
0.156
0.122
BERTScore
0.576
0.505
0.399
0.024
0.008
0.006
0.300
0.256
0.202
MoverScore
0.414
0.347
0.271
0.054
0.044
0.036
0.234
0.195
0.153
FacICC
0.416
0.484
0.376
0.297
0.259
0.212
0.356
0.371
0.294
QAGS
0.545
0.175
0.375
BARTScore
0.735
0.680
0.557
0.184
0.159
0.130
0.459
0.420
0.343
CTC
0.619
0.564
0.450
0.309
0.295
0.242
0.464
0.430
0.346
UniEval
0.682
0.662
0.532
0.461
0.488
0.399
0.571
0.575
0.465
G-EVAL-3.5
0.477
0.516
0,410
0.211
0.406
0.343
0.344
0.461
0.377
G-EVAL-4
0.631
0.685
0.591
0.558
0.537
0.472
0.599
0.611
0.525
Table 3: Pearson (r), Spearman (p) and Kendall-Tau (7) correlations of different metrics on QAGS benchmark.
recent studies find even powerful LLMS also suffer
from the problem of hallucination. This motivates recent research to design evaluators for measuring the consistency aspect in summarization (Kryš- ciñski et al., 2020; Wang et al., 2020; Cao et al., 2020; Durmus et al., 2020). We test the QAGS meta-evaluation benchmark, which includes two different summarization datasets: CNlll and XSum (Narayan al., 2018) Table 3 shows that BARTScore performs well on the more ex- tractive subset (QAGS-CNM CNN),, but has low correla- tion on the more abstractive subset (QAGS-Xsum). UniEval has good correlation on both subsets of the data.
3.95
3.8
Humen
Human Summary is Botter
LLM Summary Better
Equally Good
Figure 2: Averaged G-EvAL-44s scores for human- written summaries and GPT-3.5 summaries, divided by human judges' preference.
On average, G-EVAL-4 outperforms all state-of- the-art evaluators on QAGS, with large margin on QAGS-Xsum. G-EVAL-3.5, on the other hand, failed to perform well on this benchmark, which indicates that the consistency aspect is sensitive to the LLM's capacity. This result is consistent with Table 1.
generated summaries (using GPT-3.5, text-davinci- 003).
The dataset can be divided in three categories: 1) human-written summaries that are rated higher than GPT-3.5 summaries by human judges, 2) human- written summaries that are rated lower than GPT-3.5 summaries by human judges, and 3) Human--ritten summaries and GPT-3.5 summaries rated equally good by human judges. We use G- EVAL-4 to evaluate the summaries in each category, and compare the averaged scores.
4 Analysis
Will G-EVAL prefer LLM--ased outputs? One concern about using LLM as an evaluator is that may prefer the outputs generated by the LLM itself, rather than the high-quality human-written texts. To investigate this issue, we conduct an experi- ment on the summarization task, where we com- pare the evaluation scores of the LLM-generated and the human-writted summaries. We use the dataset collected in Zhang et al. (2023), where they first ask freelance writers write high-quality sum- maries for news articles, and then ask annotators to compare human- Human--ritted written summaries and LLM-
The results are shown in Figure 2. We can see that, G-EvAL-4 assigns higher scores to human- written summaries when human judges also pre- fer human-written summaries, and assigns lower scores when human judges prefer GPT-3.5 sum- maries. However, G-EVAL.-4 always gives higher scores to GPT-3.5 summaries than human-writter
We use G-EvAL-4 in this experiment, because its su- periority in evaluating summarization tasks. Although it has different distribution with with GPT-335, the two L.LMs should share similar behaviors in terms of text generation.

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summaries, even when human judges prefer human- written summaries. We propose two potential rea- sons for this phenomenon:
scoring without probabilities can lead to many ties, which are not counted as either concordant or dis- cordant. This may result in a higher Kendall-Tau correlation, but it does not reflect the model's 's true capacity of evaluating the generated texts. On the other hand, probability normalization can obtain more fine-grained, continuous scores that better capture the subtle difference between generated texts. This is reflected by the higher Spearman cor- relation of G- G-EVAL-4 with probabilities, which is based on the rank order of the scores.
1. NLG outputs from high-quality systems are in natural difficult evaluate. The authors of the original paper found that inter-annotator agreement on judging LLM-generated summaries is very low, with Krippendorff's alpha at 0.07.
and
2. G-EVAL may have a bias towards the LLM- generated summaries because the model could share the same concept of evaluation criteria during generation and evaluation.
The Effect of Different LLMs We compare the performance of G-EvAL with different LLMs on the SummEval and QAGS benchmarks. Ta- ble and Table 3 show that G-EvAL.4 has higher correlation than G-EvAL-3.55 on most di- mensions and datasets, except for engagingness and groundedness on the Topical-Chat bench- mark. This demonstrates that better LLM improve the performance of G-EVAL, especially for more challenging and complex evaluation tasks, such as consistency and relevance.
Our work should be considered as a preliminary study on this issue, and more research is needed to fully understand the behavior of LLM- -bsed evaluators to reduce its inherent bias towards LLM- generated text. We highlight this concern in the context that LLM-based evaluators may lead to selfrreinforcement of LLMs if the evaluation score is used reward signal for further tuning. And this could result in the over-fitting of the LLMs to their own evaluation criteria, rather than the true evaluation criteria of the NLG tasks.
5 Related Work
Ngram-based Metrics Ngram-based metrics re- fer to the scores for evaluating the NLG models by measuring the lexical overlap between generated text and reference text. BLEU (Pupineni et al., 2002) is the most widely used metric for machine translation evaluation, which calculates the geomet- ric mean of modified n-gram precision and a brevity penalty. ROUGE (Lin, 2004) is recall-oriented metric for summarization evaluation, which mea- sures the n-gram overlap between a generated sum- mary and set of reference summaries. It has been shown that more than 60% of recent papers on NLG only rely on ROUGE or BLEU evaluate their systems (Kasai et al., 2022). However, these metrics fail to measure content quality (Reiter and Belz, 2009) or capture syntactic errors (Stent et al.. 2005), and therefore do not reflect the reliability of NLG systems accurately.
The Effect of Chain-of-Thoughts We compare the performance of G-EvAL with and without chain-of-thoughtt (Co'T) on the SummEval bench- mark. Table shows that G-EvAL-4 with CoT has higher correlation than G-EVAL-4 without CoT on all dimensions, especially for fluency. This suggests that CoT can provide more context and guidance for the LLM to evaluate the generated text, and can also help to explain the evaluation process and results. And it is shown that CoT is more useful on consistency and fluency dimen- sions. We also provide results of G-EVAL with simple prompting baseline on SummEval (only asking GPT-4 score summary from 1-5 on each dimension, without detailed task introduction, evaluation criteria and CoT).
The Effect of Probability Normalization We compare the performance of G-EVAL with and without probability normalization on the Sum- mEval benchmark. Table shows that, on Kendall- Tau correlation, G-EVAL-4 with probabilities is inferior to G-EVAL-4 without probabilities on Sum- mFval. We believe this is related to the calculation Kendall-TTe Tau correlation, which is based on the number of concordant and discordant pairs. Direct
Embedding based Metries Embedding metrics refer to the scores for evaluating the NLG models by measuring the semantic similarity be- tween generated text and reference text based on the word sentence embeddings. WMD (Kus- ner et al., 2015) is a metric that measures the dis- tance between two texts based on the word embed- dings. BERTScore (Zhang et al., 2019) measures

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the similarity between two based on the con- textualized embedding from BERT (Devlin al., 2019). MoverScore (Zhao et al., 2019) improves BERTScore by adding soft alignments and new aggregation methods to obtain a more robust simi- larity measure. (Clark et al., 2019) propose metric that evaluates multi-sentence texts by computing the similarity between the generated text and the reference text based on the sentence embeddings.
extensive experiments on NLG tasks, text sum- marization and dialogue generation, and show that G-EVAL can outperform state-of-the-art evaluators and achieve higher human correspondence. We also propose preliminary analysis on the behavior of LLM-based evaluators, and highlight the poten- tial issue of LLM-based evaluator having bias towards the LLM-generated texts. We hope our work can inspire
research on using LLMs for NLG evaluation, and also raise awareness of the potential risks and challenges of using LLMs as evaluators.
Tpsk-specific Evaluators spec-ip metrics refer to the scores for evaluating the NLG mod- els by measuring the quality of the generated texts based on the specific task requirements. For ex- ample, summarization tasks need to assess the consistency of the generated summaries (Kryš- ciński al., 2020; Wang et al., 2020; Cao al., 2020; Durmus et al., 2020), and dialogue response generation tasks need assess the coherence of the generated responses (Dziri et al., 2019; Ye et al., 2021; Ghazarian et al., 2019). However, these met- rics are not generalizable to other NLG tasks, and
Limitations
G-EVAL framework that uses LLMs evaluate the quality of generated texts. However, it also has some limitations that need to be addressed in future work.
1. As we already discussed in the paper, G-EVAL may have bias towards the LLM-generated texts. This may lead to the self-reinforcement of LLMs if the evaluation score is used a reward signal for further tuning. And this could result in the over-fitting of the LLMs to their own evaluation criteria, rather than the true evaluation criteria of the NLG tasks.
they are not able to measure the overall quality of the generated texts.
Unified Evaluators Recently, some evaluators have been developed to assess text quality from multiple dimensions by varying the input and out- put contents (Yuan et al., 2021) or the model vari- ants (Mehri and Eskenazi, 2020) they use. Vnilvaa (Zhong et al., 2022) is unified evaluator that can evaluate different aspects of text generation QA tasks. By changing the question format, it can han- dle different evaluation tasks.
2. G-EvAL. is limited by the availability and ac- cessibility of LLMs. Currently, most LLMs are not publicly available, and require special access or payment to use. This may limit the applicability and reproducibility of G-EvAL. Moreover, the LLMs are constantly updated, which may lead inconsistent evaluation re- sults across different versions of the LLMs.
LLM-based Evaluators Fu et al. (2023) propose GPTScore, a new framework that evaluated texts with generative pre-training models like GPT-3. It assumes that generative pre- pre-training model will assign higher probability of high-quality gener- ated text following given instruction and context. Wang et al. (2023a) conduct preliminary survey using ChatGPT as a NL.G evaluator. Koemi and Federmann (2023); Lu
We meta-cvaluate G-EvAL on two NLG tasks, text summarization and dialogue generation. However, there are some emerging NLG tasks in the LLM era where users prompt with free- form natural language instructions. In this case, the evaluation criteria may need to be more flexible and adaptive the user's inten- tion and preference. Therefore, more research needed to explore how to use G-EVAL for evaluating these new types of NLG tasks.
(2023) proposed to use GPT models for evaluating machine translation tasks. Very recently, Wang et al. (2023b) investi- gated the problem of unfairness when using large models in evaluating dialogue responses.
6 Conclusion
Ethics Statement
In this paper, we propose G-EVAL,, a framework of using LLM with chain-of-thouge (CoT) to eval- uate the quality of generated texts. We conduct
The G-EVAL framework we proposed is designed to offer more effective and reliable method for assessing natural language generation systems. Its

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purpose is aid researchers, developers, and other interested parties in evaluating the quality of text produced by NLG systems. Possible risks could exist if G-EVAL is unable to precisely evaluate the quality of produced texts or shows a preference for LLM-created texts. This could lead to developers overestimating the performance of their systems unintentionally reinforcing biases in their mod- els. Furthermore, users depending on the generated material may receive low-quality or biased infor- mation.
Alexander R Fabbri, Wojeiech Kryšcińsski Bryan Mc- 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.
Jinlan Fu, See-Kiong Ng. Zhenghao Jiang, and Pengfei Liu. 2023. Gptscore: Evaluate as you desire. arXiv preprint arXiv:2302.04160
Sarik Ghazarian, Johnny Wei, Aram Galstyan, and Nanyun Peng. 2019. Better automatic evaluation of open-domain dialogue systems with contextual- ized embeddings. In Proceedings of the Workshop on Methods for Optimizing and Evaluating Newral Language Generation, pages 82-89, Minneapolis, Minnesota. Association for Computational Linguis- tics.
References
Satanjeev Banerjee and Alon Lavie. 2005, Meteor: An automatic metric for mt evaluation with improved cor- relation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or sunmariza- rion, pages 65-72.
Karl Moritz, Hermann, Tomas Kocisky, Edward Grefen- stette, Lasse Esseholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. Advances in neural information processing systems, 28.
Meng Cao, Yue Dong, Jiapeng Wu, and Jackie Chi Kit Cheung. 2020. Factual error correction for abstrac- tive summarization models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 625 16258.
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander Fabbri, Yejin Choi, and Noah A. Smith. 2022. Bidimensional leaderboards: Generate and evaluate language hand in hand. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguisties: Human Language Tech- nologies, pages 3540-3551 Seattle, United States. Association for Computational Linguisties
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018. Faithful to the original: Fact aware neural abstractive summarization. In thirty-second AAAI conference on artificial intelligence.
Elizabeth Clark, Asli Celikyilmaz, and Noah A Smith. 2019. Sentence mover's 's similarity: Automatic evalu- ation for multi-sentence texts. In Proceedings of the 57th Annual Meeting of the Association for Compu- tational Linguistics, pages 2748-2760.
Tom Kocmi and Christian Federmann. 2023. Large language models are state-of-the-art evaluators of translation quality, arXiv preprint arXiv:2302. 14520.
Wojeiech Kryácińssii, Bryan McCann, Caiming Xiong, and Richard Socher. 2020. Evaluating the factual consistency of abstractive text summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9332-933
Jacob Devlin, Ming- Wei Chang, Kenton Lee, and Kristina Toutanova. 2019, Bert: tre-training of deep bidirectional transformers for language understand- ing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Com- putational Linguistics: Human Language Technolo- gies, Volume 1 (Long and Short Popers), pages 4171- 4186.
Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Wein- berger. 2015, From word embeddings to document distances. In International conference on machine learning, pages 957-966. PMLR.
Esin Durmus, He He, and Mona Diah. 2020. Feqa: A question answering evaluation framework for faith- fulness assessment in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics. pages 5055- 5070.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: denoising sequence-to-sqqeene pre-training for natural language generation, translation, and com- prehension. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 7871-7880. Association for Computational Linguisties.
Nouha Dziri, Ehsan Kamalloo, Kory Mathewson, and Osmar R Zaiane. 2019. Evaluating coherence in di- alogue systems using entailment. In Proceedings of the 2019 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, Volume (Long and Short Papers), pages 3806-3812.
Chin-Yew Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81.

---

Qingyu Lu, Baopu Qiu, Liang Ding, Liping Xie, and Dacheng Tao. 2023. Error analysis prompting en- ables human-like translation evaluation in large lan- guage models: A case study on chatgpt. arXiv preprint arXiv:2303.1380
Peiyi Wang. Lei Li, Liang Chen, Dawei Zhu, Binghuai Lin. Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. 2023b. Large language models are not fair evaluators. arXiv preprint arXiv:2305.1733
Jason Wei, Xuezhi Wang. Dale Schuurmans, Maarten Bosma, Ed Chi, Quoe Le, and Denny Zhou, 2022. Chain of thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 28.
Shikib Mehri and Maxine Eskenazi. 2020. USR: An unsupervised and reference free evaluation metric for dialog generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 681-707, Online. Association for Computational Linguistics.
Zheng Ye, Liucun Lu, Lishan Huang, Liang Lin, and Xiaodan Liang. 2021. Towards quantifiable dialogue coherence evaluation. In Proceedings of the 59th An- nual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer- ence on Natural Language Processing (Volume 1: Long Papers), pages 2718 2729.
Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018. Don't t give me the details, just the summary! topic-aware convolutional neural networks for ex- treme summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Lan- guage Processing, pages 1797-1807.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instruc- tions with human feedback. Advances in Neural Information Processing Systems. 35:27730-27744
Weizhe Yuan, Graham Neubig. and Pengfei Liu. 2021. Bartscore: Evaluating generated text as text gener- ation. Advances in Neural Information Processing Systems, 34.
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Eval- uating text generation with bert. arXiv preprint 1904v:190..09675.
Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: method for automatic evalu- ation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computa- tional Linguistics, pages 311-318.
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang- Kathleen McKeown, and Tatsunori B. Hashimoto. 2023, Benchmarking large language models for news summarization.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text trans- former. Journal of Machine Learning Research, 21:1- 67.
Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Chris- tian M Meyer, and Steffen Eger. 2019. Moverscore: Text generation evaluating with contextualized em- beddings and earth mover distance. In Proceedings of the 2019 Conference Empirical Methods in Nat- ural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-JJCNLP) pages 563-578.
Ehud Reiter and Anja Belz. 2009. An investigation into the validity of some metrics for automatically evalu- ating natural language generation systems. Computa- tional Linguistics, 359455529-558
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang. Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and lon Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena.
Amanda Stent, Matthew Marge, and Mohit Singhai. 2005. Evaluating evaluation methods for generation the presence of variation. In Proceedings of the 6th international conference Computational Linguis- tics and Intelligent Text Processing, pages 341-351.
Rohan Taori, Ishaan Guirajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction- following Iaama model. https:// gi github.
Ming Zhong, Yang Liu, Da Yin. Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, and Jiawei Han. 2022. Towards a unified multi- dimensional evaluator for text generation. In Pro- ceedings of the 2022 Conference on Empirical Meth- ods in Natural Language Processing, pages 2023- 2038, Abu Dhabi, United Arab Emirates.
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020. Asking and answering questions to evaluate the fac- tual consistency of summaries. In Proceedings of the 58th Annual Meeting of the Association for Compu- tational Linguistics. pages 5008-5020.
A Example Prompts
Evaluate Coherence in the Summarization Task
Jiaan Wang, Yunlong L Liang, Fandong Meng, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, and Jie Zhou. 2023a. chatgpt good nig evaluator? preliminary study. arXiv preprint arXiv:2303.0400
You will be given one summary written for a news article.

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Your task is rate the summary on one metric.
Your task is rate the responses on one metric.
Please make sure you read and under- stand these instructions carefully. Please keep this document open while reviewing, and refer to it needed.
Please make sure you read and under- stand these instructions carefully. Please keep this document open while reviewing, and refer it needed.
Evaluation Criteria:
Evaluation Crieteria:
Coherence (1-5) the collective quality of all sentences. We align this dimension with the DUC quality question of structure and coherence whereby "the summary should be well-structured and well- well-organized. The summary should not just be a heap of related information, but should build from sentence to sentence to a coherent body of information about topic."
Engagingness (1-3) Is the response dull/interestingg?
A score of (dull) means that the re- sponse is generic and dull.
A score of 2 (somewhat interesting) means the response is somewhat inter- esting and could engage you in the con- versation (e.g., s., an opinion, thought)
A score of 3 (interesting) means the response is very interesting or presenus an interesting fact
Evaluation Steps:
1. Read the news article carefully and identify the main topic and key points.
Evaluation Steps:
1. Read the conversation, the correspond- ing fact and the response carefully.
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 a clear and logical order.
2. Rate the response a scale of 1-3 for engagingness, according to the criteria above.
3. Assign score for coherence on a scale of to 5, where is the lowest and is the highest based on the Evaluation Criteria.
3. Provide a brief explanation for your rating, referring to specific aspects of the response and the conversation.
Example:
Example:
Conversation History:
Source Text:
[(Document)
((Document))
Corresponding Fact:
Summary:
([Fact))
([Summary])
Response:
[[eesponse])
Evaluation Form (scores ONLY):
-Coherence:
Evaluation Form (scores ONLY):
Evaluate Engagingness in the Dialogue Genera- tion Task
-Engagingnes::
Evaluate Hallucinations
You will be given conversation between two individuals. You will then be given one potential response for the turn in the conversation. The response con- cerns an interesting fact, which will be provided as well.
Human Evaluation of Text Summariza- tion Systems:
Factual Consistency:
Does the
summary untruthful or misleading facts

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that are not supported by the source text?
Source Text:
((Document)
Summary:
[(Summary]
Does the summary contain factual inconsistency?
Answer:


Reviews:
[{"review_id": "E64HyMIGYB", "reviewer": "Reviewer_LBER", "summary": "", "questions": "First, I commend the authors for their innovative approach utilizing GPT-4. The paper is well structured and the contributions are interesting. However, to fully understand the scope, impact and future direction of this research, I have a few questions:\n\n1. How do you avoid the instability or unreliability of GPT-4 and GPT-3.5 as metrics? As far as I know, there have been such studies on other LLM fine-tuning methods.\n\n2. I would like to see how much your method improves on a GPT-4 baseline that doesn't use your method at all, not just compared to other metrics (like BertScore, Rouge......), because I don't know if the performance improvement is from GPT-4 or your method. Can you show me this result? Just need a simplest prompting when using GPT-4.", "limitations": "", "rating": 3, "confidence": 5, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer,\n\nThank you for your insightful comments and constructive feedback. We would like to address your concerns as follows:\n\n1. Unreliability of GPT-4: We acknowledge your concern about the potential instability of GPT-4. However, we believe that the \"gradual degradation\" of GPT-4 is not a significant issue for our method. The G-EVAL framework is designed to be adaptable to different LLMs, and we chose GPT-4 for our experiments due to its state-of-the-art performance. If GPT-4's performance degrades over time, we can easily switch to a different LLM. Meanwhile, we re-ran experiment with the latest GPT-4-0613 and get the results on SummEval:\n\n|  | Coh. ($\\rho$)  | Coh. ($\\tau$)  | Con. ($\\rho$) | Con. ($\\tau$)  | Flu. ($\\rho$) |Flu.    ($\\tau$)   | Rel. ($\\rho$) | Rel .($\\tau$)  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| G-EVAL-4 | 0.582 | 0.457 | 0.507 | 0.425 | 0.455 | 0.378 | 0.547 | 0.433 |\n| G-EVAL-4(0613) | 0.593 | 0.462 | 0.508 | 0.425 | 0.465 | 0.382 | 0.545 | 0.430 |\n\nWe can see that GPT-4 is not getting worse as an evaluator.  We will clarify this point in the revised manuscript. We will also report meta-evaluation results of different versions of GPT-4 to discuss this point.\n\nMeanwhile, we want to emphasize that \"as a metric for evaluation, the stability of the results must be guaranteed\" is only applied to conventional automatic metrics, but not to human evaluation (different human annotators will have different judges of the same output). And with more and more complex NLG tasks proposed, we believe frameworks like G-EVAL which is designed to mimic human evaluation, should not be constrained by this. Instead, we could use a pre-meta-evaluation test to make sure the evaluator's performance is reasonable. We appreciate your insightful comments on this point and will add more discussion in the limitation section.\n\n\n2. Lack of baseline comparison: We agree that a comparison with a simple GPT-4 baseline would be beneficial. We did a simple prompting baseline on SummEval (only asking GPT-4 to score a summary from 1-5 on each dimension, without detailed description and CoT)\n\n|  | Coh. ($\\rho$)  | Coh. ($\\tau$)  | Con. ($\\rho$) | Con. ($\\tau$)  | Flu. ($\\rho$) |Flu.    ($\\tau$)   | Rel. ($\\rho$) | Rel .($\\tau$)  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| G-EVAL-4 | 0.582 | 0.457 | 0.507 | 0.425 | 0.455 | 0.378 | 0.547 | 0.433 | \n| GPT-4(simple prompt) | 0.513 | 0.424 | 0.421 | 0.344 | 0.447 | 0.373 | 0.479 | 0.388 | \n\nWe will include these results in the revised manuscript to provide a more comprehensive comparison.\n\n3. Regarding the error in Figure 1, we appreciate your attention to detail and will correct this in the revised version.\n\nWe believe that our responses address your concerns and we hope that you will reconsider the paper. We are dedicated to making improvements to the paper based on your valuable input.", "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": "This article provides new perspectives and methods for the long-standing problem of how to evaluate the output of natural language generation systems, which is of great significance for the widespread use of current NLG applications.\n\n1. Innovative approach: The G-EVAL framework utilizes large language models with \"CoT and form-filling paradigms\", which demonstrate better evaluation than traditional metrics such as BLEU and ROUGE. G-EVAL has potential applications across multiple NLG tasks, such as text summarization and dialogue generation.\n\n2. Extensive experimental verification: The paper conducts extensive experiments, which compares it with existing methods and shows its superiority.\n\n3. Discussion of potential problems: In addition to proposing new methods, the author also analyzes the problems that may be encountered when using LLM as an evaluator, which provides an important reference for related research.\n\nBenefits to the NLP community:\n\nProvide a new evaluation tool: G-EVAL can provide the NLP community with a new NLG evaluation tool.", "reasons_to_reject": "Although this article uses GPT-4 as a new metric for NLG evaluation, two questions make me question:\n\n1. The unreliability of GPT-4 is not considered:\nAs a metric for evaluation, the stability of the results must be guaranteed, that is, the same dataset, when using this metric at any time (such as the past three months, now, next year...) is guaranteed to obtain the same results. Obviously, I don't see this in your method, because some recent studies have shown that GPT-4 is gradually getting stupid (https://arxiv.org/pdf/2307.09009.pdf ,  https://github.com/THU-KEG/ChatLog ), which means that when using GPT-4 in the text as the skeleton and using the same parameters, I can't reproduce your results after a while, then it loses the meaning of the metric. Of course one solution is to use other LLMs for fine-tuning as the skeleton, but I don't see this in this paper.\n\n2. Lack of baseline comparison: The G-EVAL method in this paper is entirely based on GPT-3.5 and GPT-4, but does not demonstrate their performance when using the simplest prompt for these two LLMs, and then compare it with the G-EVAL method. ", "questions_to_authors": "First, I commend the authors for their innovative approach utilizing GPT-4. The paper is well structured and the contributions are interesting. However, to fully understand the scope, impact and future direction of this research, I have a few questions:\n\n1. How do you avoid the instability or unreliability of GPT-4 and GPT-3.5 as metrics? As far as I know, there have been such studies on other LLM fine-tuning methods.\n\n2. I would like to see how much your method improves on a GPT-4 baseline that doesn't use your method at all, not just compared to other metrics (like BertScore, Rouge......), because I don't know if the performance improvement is from GPT-4 or your method. Can you show me this result? Just need a simplest prompting when using GPT-4.", "paper_topic_and_main_contributions": "G-EVAL delves into the challenges of evaluating the quality of text generated by NLG systems. Traditional metrics, such as BLEU and ROUGE, often have low correlation with human judgment, especially in tasks that require creativity and diversity in generated text. This paper focuses on enhancing the evaluation metrics of NLG systems. The proposed G-EVAL framework is an innovation with excellent performance and promises for more consistent and accurate evaluation of generated text. \n\nThe main contributions of this paper are:\n1. G-EVAL framework: The author introduces the G-EVAL framework, a method that adopts LLM as an evaluation indicator, especially with the \"thought chain\" and form filling paradigm. This more effectively evaluates the quality of NLG output.\n2. Multiple task experiments: This study conducted extensive experiments on tasks such as text summarization and dialogue generation, demonstrating the versatility of the method.\n3. Excellent performance: The performance of the G-EVAL with GPT-4 as the backbone surpasses the previous method on three tasks.\n4. LLM-based evaluator analysis: Section 4 of this paper will provide an interesting finding on the potential bias of LLM-generated text.", "excitement": 3, "reproducibility": 1, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None.", "Typos_Grammar_Style_and_Presentation_Improvements": "1. There is an error in the third subsection of the Evaluation Steps section of Figure 1, which should be 1 to 5, not 1 to 10. Otherwise the meaning does not match the other parts.", "Justification_for_Ethical_Concerns": "None."}, "extra_text": {}}, {"review_id": "hbmITaqKiL", "reviewer": "Reviewer_EnEs", "summary": "", "questions": "(A) Does the evaluating LLM ever actually have to perform the different evaluation steps that it generates? If not, this feels somewhat different from the original Chain of Thoughts setup. These are also pretty high-level steps compared to typical CoT... Am I missing something?\n\n(B) Why are some of the listed baselines not shown in table 2?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer,\n\nThank you for your insightful comments and suggestions. We appreciate your time and effort in reviewing our paper. We would like to address your concerns as follows:\n\n1. Comparison with other text generation evaluators: We agree with your point that a comparison with other evaluators using the same LLMs would provide a more comprehensive evaluation of GEval. But we want to emphasize due to the API limit, GPT 3.5 is already the “the latest version of GPT that GPTScore can support”. And for a more detailed ablation of G-Eval we did a simple prompting baseline on SummEval (only asking GPT-4 to score a summary from 1-5 on each dimension, without detailed description and CoT)\n\n|  | Coh. ($\\rho$)  | Coh. ($\\tau$)  | Con. ($\\rho$) | Con. ($\\tau$)  | Flu. ($\\rho$) |Flu.    ($\\tau$)   | Rel. ($\\rho$) | Rel .($\\tau$)  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| G-EVAL-4 | 0.582 | 0.457 | 0.507 | 0.425 | 0.455 | 0.378 | 0.547 | 0.433 |\n| GPT-4(simple prompt) | 0.513 | 0.424 | 0.421 | 0.344 | 0.447 | 0.373 | 0.479 | 0.388 |\n\nWe will include these results in the revised manuscript to provide a more comprehensive comparison.\n\n\n2. The role of the evaluating LLM: The evaluating LLM does not need to explicitly perform the different evaluation steps that it generates. The steps are used as a guide to structure the evaluation process and ensure that all relevant aspects of the task are considered. This can be considered a simple version of CoT with only one step. We will clarify this point in the revised manuscript.\n\n3. Baselines not shown in Table 2: Table 2 is for Topical-Chat benchmark which evaluates dialogue generation tasks, and some baselines are designed only for evaluating summarization models. We have double-checked and all dialogue-related baselines are included in Table 2. We will clarify this in the next version.\n\n4. Regarding the claim about the effect of model size: We agree that the comparison between GPT3.5 and GPT4 may not fully represent the effect of model size due to potential differences in the training process. We will revise this statement to more accurately reflect the limitations of our comparison.\n\nWe hope that our responses address your concerns and we hope that you will reconsider the paper. We are dedicated to making improvements to the paper based on your valuable input.", "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": "A lot of the analysis in section 4 is strong and provides some really useful insights that future work can build off of, particularly the experiments that examine the cases in which LLMs seem to prefer LLM text to human-written texts. (If I had to nitpick, the section could perhaps do with an acknowledgment that aspects of how the human-written data was gathered may have also contributed to relatively low quality would be useful to add as a caveat, but the analysis is already useful as is.) The discussion of how the formulation of the Kendall-Tau correlation might affect the interpretation of results at first glance is also useful to note.\n\nThe proposed method (using GPT3.5 and GPT4) seems to outperform various existing baselines.", "reasons_to_reject": "Given that the framework of GEval is LLM-agnostic, and GEval is the main contribution of the paper, it seems really important to compare GEval to at least one other text generation evaluator using the same LLM(s) in order to evaluate the benefits conferred by the framework itself, and I don't think that's currently happening? I understand that GPTScore requires information that's not currently given out by the OpenAI API for GPT4, but in that case, it seems important to evaluate GEval on the latest version of GPT that GPTScore can support as well, to allow for a fair comparison to GPTScore. I think that the reported GPTScore performance just uses GPT3, though, which GEval doesn't. As it is, I'm not sure how much of the improved correlation with human judgment is due to GEval, or just to the use of GPT3.5/GPT4.", "questions_to_authors": "(A) Does the evaluating LLM ever actually have to perform the different evaluation steps that it generates? If not, this feels somewhat different from the original Chain of Thoughts setup. These are also pretty high-level steps compared to typical CoT... Am I missing something?\n\n(B) Why are some of the listed baselines not shown in table 2?", "paper_topic_and_main_contributions": "This paper proposes an evaluation framework for models that generate text. The proposed evaluation framework, GEval, uses a LLM to first generate intermediate evaluation steps based on the task description. Then, the LLM's output is appended to a fixed prompt, and then has input context and the target text appended to it, with only a simple form that just asks for numerical scores after that. (I *think* this is right, please correct me if my read of this part of the paper got something wrong.) Different token output options for numerical scores by the LLM are then combined in a weighted average to produce a final score.\n\nFor the paper's experiments, they use GPT3.5 and GPT4 as the LLM for GEval, and they compare to a variety of baselines, including GPTScore. They evaluate on summarization and dialogue generation datasets, using correlation with human judgments as the metric of interest, finding that GEval-4 generally performs best.\n\nThe paper then performs an analysis of whether LLMs seem to have a bias towards preferring LLM-generated text, finding that this is indeed somewhat of an issue. There's then a discussion of how to interpret some of the Kendall-Tau based results reported earlier.", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "I think it's a bit of a stretch to claim that comparing results from GPT4 to those from GPT3.5 tells you \"the effect of model size\" (line 456). Since not every detail of the training process for OpenAI's models is published, there are enough unknown differences between the two models to make the comparison between these two models not necessarily representative of what happens as you vary model size in general.\n\nThe described setup doesn't quite seem like Chain of Thought to me? But this depends on the answer to question (A), I wasn't left completely sure about that."}}, {"review_id": "KWyxsCSs7m", "reviewer": "Reviewer_qnnB", "summary": "", "questions": "1.\n\nLines 38-40: \"but they have been shown to have relatively low correlation with human judgments, especially for open-ended generation tasks\".  What are the typical correlations for those? Maybe provide  references to papers.\n\n2.\n\nFor the experiments conducted, human evaluations were used from previous research.\nWhat scales were used by human evaluators?  1-5 ? \nSame scales for all dimensions of evaluation?\nThis should be explicitly described in the current paper. \n\n3.\n\nWhen comparing human and  G-EVAL-without-probability-normalization (i.e. just the integer rating scales),\nyou should try  the  QWK metric (quadratic weighted kappa),\nThis measure of agreement is often used in Automated Essay Scoring literature for comparing human and automated scores.\nIt will also 'control' for chance agreement. \n\n\n\n ", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer,\n\nThank you for your constructive feedback and positive comments on our paper. We appreciate your time and effort in reviewing our work. We would like to address your questions and concerns as follows:\n\n1. Regarding your question about the typical correlations for open-ended generation tasks, the correlations vary depending on the specific task and evaluation metric used. For instance, UniEval reported a correlation of 0.128 between ROUGE-L and human evaluation for summarization tasks, which is usually considered as low correlation. We will add these references and more specific information in the revised manuscript to provide a clearer context.\n\n2. Concerning the scales used by human evaluators in the experiments, SummEval used a scale of 1-5, TopicalChat used a scale of 1-3, Fact-based Benchmark used a scale of 0-1. We will explicitly describe this in the revised manuscript to avoid any confusion.\n\n3. We appreciate your suggestion to use the QWK metric. We agree that this measure could provide a more robust comparison by controlling for chance agreement. We will incorporate this metric in our revised manuscript and discuss the results.", "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": "A good and creative idea and well-designed experimental work.", "reasons_to_reject": "n/a", "questions_to_authors": "1.\n\nLines 38-40: \"but they have been shown to have relatively low correlation with human judgments, especially for open-ended generation tasks\".  What are the typical correlations for those? Maybe provide  references to papers.\n\n2.\n\nFor the experiments conducted, human evaluations were used from previous research.\nWhat scales were used by human evaluators?  1-5 ? \nSame scales for all dimensions of evaluation?\nThis should be explicitly described in the current paper. \n\n3.\n\nWhen comparing human and  G-EVAL-without-probability-normalization (i.e. just the integer rating scales),\nyou should try  the  QWK metric (quadratic weighted kappa),\nThis measure of agreement is often used in Automated Essay Scoring literature for comparing human and automated scores.\nIt will also 'control' for chance agreement. \n\n\n\n ", "paper_topic_and_main_contributions": "The manuscript addresses the topic of evaluating NLG systems.\nIt continues a recent trend of using LLMs for NLG evaluation.\nThe specific proposal in this paper is to use Chain-of-Thought (CoT) prompting as an intermediate step in the evaluations.\nThe authors use data from two NLG tasks - summarization and dialog generation, and demonstrate that their proposed evaluation system\nachieves better correlations with human evaluations than classic approaches like ROUGE or recent LLM-based approaches.  \n", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "n/a", "Justification_for_Ethical_Concerns": "no evident ethical concerns "}, "extra_text": {}}]

Author rebuttal:

Meta-review: The reviewers are generally in agreement about the quality of the idea and execution of the experiments in this paper. The one point of disagreement is one reviewer's concern about the unreliability of GPT-based metrics; however (as another reviewer and the authors point out) reliability is a challenge across many of today's evaluation practices (including human evaluations). The authors agreed to discuss this topic in more depth, particularly in the limitations section, which I think is sufficient, given the scope of the paper.
Decision: Accept-Main