# A Closer Look into Automatic Evaluation Using Large Language Models

Cheng-Han Chiang
National Taiwan University,
Taiwan
<EMAIL_ADDRESS>
&Hung-yi Lee
National Taiwan University,
Taiwan
<EMAIL_ADDRESS>

###### Abstract

Using large language models (LLMs) to evaluate 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 paper, we analyze LLM evaluation (Chiang and Lee, 2023)111In this paper,
the term LLM evaluation is used to refer to the specific method proposed by
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-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.

##  1 Introduction

Large language models (LLMs) trained with task instructions and human feedback
can follow natural language instructions to complete a 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 et al., 2023). By simply instructing the LLMs on how to rate and
giving the LLMs the sample to be rated, the LLM can follow the instructions
and provide a rating of the sample.

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 are aligned to the evaluation results of humans. Still, LLM evaluation
and G-Eval differ in some specific design choices in the evaluation procedure.
Since Chiang and Lee (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 determine
how to conduct an automatic evaluation using LLMs.

Given that LLM evaluation and G-Eval have already received significant
attention shortly after publication, these methods will likely revolutionize
the evaluation in NLP. Therefore, conducting a 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 ratings. Based on our analysis, we provide guidelines
on how to use LLMs for automatic evaluations. We have the following findings:

  * •

Auto-CoT (proposed by G-Eval) does not always improve the correlation between
LLM and human ratings.

  * •

Making the LLMs output only a single numeric rating is suboptimal.

  * •

Asking the LLMs to rationalize their own ratings significantly improves the
correlation between the LLMs’ ratings and human ratings.

  * •

On two datasets, we improve the best correlation that ChatGPT’s rating can
achieve, and some correlations even exceed prior SoTA correlations obtained
using the ratings of GPT-4 in Liu et al. (2023).

##  2 Experiment Setup

Our paper studies what components in LLM evaluation and G-Eval make the
ratings generated by LLM correlate with human ratings better, and we aim to
improve the correlation.

###  2.1 LLM as an Automatic Evaluation Metric

Both LLM evaluation (Chiang and Lee, 2023) and G-Eval (Liu et al., 2023)
propose to ask LLMs to rate a sample regarding some attributes of the sample
(e.g., fluency, grammaticality) using a $k$-point Likert scale. They give the
LLMs (1) 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) a
sentence that prompts the LLM to give the rating222In our paper, we use
different highlight colors to represent different parts of the prompt, as
shown in the above text. Additionally, we use cyan to represent the parts
generated by auto Chain-of-Thought. 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.

Difference 1: Auto Chain-of-Thought The task descriptions and rating criteria
in LLM evaluation and G-Eval are all human-written. However, 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:" to prompt the LLM. The
LLM then generates an ordered list containing the step-by-step evaluation
steps. They dub this process auto chain-of-thought (CoT). G-Eval uses human-
written task instructions and auto-CoT-generated 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):" to restrict the LLM to output only
the numeric rating; the placeholder will be replaced by the evaluated
attributes. In contrast, LLM evaluation uses the following 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.

###  2.2 Meta-Evaluating an Evaluation Metric

Given a sample, an evaluation metric assigns it a 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 metric and the human ratings. A higher correlation
indicates the evaluation metric better aligns with human ratings. We adopt
Pearson $r$ and Kendall’s $\tau$ as they are widely used in meta-evaluations
(Graham 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 calculation of correlation
coefficients are in Appendix C.

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 a meta-evaluation dataset for summarization derived
from the CNN/DailyMail dataset (Hermann et al., 2015). Each summary in
SummEval 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 a dataset that evaluates the quality of a response given the
dialogue history and a piece of knowledge relating to the dialogue. We follow
Zhong et al. (2022) to evaluate the naturalness, coherence, engagingness, and
groundedness (whether the response is grounded on the provided knowledge) of
the response. The dataset details are in Appendix E.

###  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-turbo-0613) (OpenAI, 2022) for evaluation since it has lower cost and
improved performance compared with other GPT-3.5 models. ChatGPT 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.

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-instruct model (Almazrouei et al., 2023), but we find it cannot
follow the instructions and rate the samples very well. Hence, we exclude open
LLMs in our paper.

Sec. | Ablations | Coherence | Consistency | Fluency | Relevance
---|---|---|---|---|---
CoT | Output | $r$ | $\tau$ | $r$ | $\tau$ | $r$ | $\tau$ | $r$ | $\tau$
GPT-4† | ?‡ | Score only | 0.581 | 0.463 | 0.575 | 0.419 | 0.6 | 0.457 | 0.599 | 0.409
| ✓ | Score only | 0.45 | 0.359 | 0.37 | 0.286 | 0.319 | 0.203 | 0.403 | 0.327
3.1 | ✗ | 0.344 | 0.248 | 0.328 | 0.185 | 0.361 | 0.177 | 0.353 | 0.248
| ✗ | Score only | 0.344 | 0.248 | 0.328 | 0.185 | 0.361 | 0.177 | 0.353 | 0.248
| ✗ | Free Text | 0.46 | 0.342 | 0.476 | 0.334 | 0.477 | 0.273 | 0.324 | 0.228
| ✗ | Rate-explain | 0.557 | 0.44 | 0.473 | 0.337 | 0.451 | 0.306 | 0.509 | 0.348
3.2 | ✗ | Analyze-rate | 0.635 | 0.476 | 0.537 | 0.34 | 0.479 | 0.302 | 0.444 | 0.305
Table 1: The Pearson’s $r$ and Kendall’s $\tau$ 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 in G-Eval as the baseline of this paper. We
boldface the Pearson’s $r$ statistically significantly higher than the
baseline (except GPT-4). $\dagger$: 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. $\ddagger$: The results of GPT-4
cannot serve as a reasonable comparison since we find something odd in the
prompts Liu et al. (2023) use, which we elaborate in Appendix A.

##  3 Better Usage of LLM for Evaluation

###  3.1 Is Auto CoT Always Useful?

Liu et al. (2023) shows that adding the evaluation steps generated by auto CoT
improves the correlation on SummEval when using GPT-4 for evaluation. 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 significant? (2) Does auto CoT yield
higher correlations for different LLMs and datasets? To answer these
questions, we use 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
Pearson’s $r$ of using and not using auto CoT is statistically significantly
different. We try to 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.

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 1 only for reference. Comparing our results with GPT-4
is unfair since we use ChatGPT, which is weaker than GPT-4. A more reasonable
baseline for our 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 ChatGPT can achieve in Liu et al. (2023). The numbers here
differ from results in Liu et al. (2023) because we 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
correlations reach statistical significance with $p$-values less than $0.05$.
However, using auto CoT results in a lower Pearson’s $r$ for fluency, and this
inferiority in Pearson’s $r$ is also statistically significant.

The results for Topical-Chat are illustrated in Table 2. For Topical-Chat, the
Pearson’s $r$ of using and not using auto CoT are very close for all four
attributes except groundedness, with differences 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 consistent and meaningful improvements compared with not using CoT. This
should not be surprising since the evaluation steps generated with auto CoT
often merely paraphrases the evaluation criterion and instructions given to
the LLM.

Sec. | Ablations | Naturalness | Coherence | Engagingness | Groundedness
---|---|---|---|---|---
CoT | Output | $r$ | $\tau$ | $r$ | $\tau$ | $r$ | $\tau$ | $r$ | $\tau$
| ✓ | Score only | 0.393 | 0.358 | 0.468 | 0.391 | 0.549 | 0.513 | 0.311 | 0.566
3.1 | ✗ | 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
| ✗ | Free Text | 0.464 | 0.476 | 0.524 | 0.426 | 0.611 | 0.557 | 0.563 | 0.666
| ✗ | Rate-explain | 0.524 | 0.47 | 0.477 | 0.416 | 0.567 | 0.524 | 0.58 | 0.693
3.2 | ✗ | Analyze-rate | 0.573 | 0.47 | 0.486 | 0.416 | 0.628 | 0.524 | 0.725 | 0.693
Table 2: The Pearson’s $r$ and Kendall’s $\tau$ correlation coefficient
between LLMs’ ratings and human ratings for Topical-Chat. All the results in
this table, except the first row, are from ChatGPT. We boldface the Pearson’s
$r$ statistically significantly higher than auto CoT + score only. We
underline the Pearson’s $r$ comparable auto CoT + score only.

###  3.2 Prompt for Outputs

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 descriptions and evaluation criteria but
differ in how they prompt the LLM to generate the output. One uses "score
only", as in G-Eval. The other replaces the "score only" with "How
{{placeholder}} is the sample? (on a scale of 1-k, with 1 being the lowest)",
as in LLM evaluation. We call the latter prompts free text since they do not
restrict the output form.

The results for SummEval are shown in the yellow blocks in Table 1, 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 $r$ and Kendall’s $\tau$
much higher than restricting the model to output a single numeric score for
almost all attributes of both datasets. The higher Pearson’s $r$ 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.

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 ratings. However, we observe
that the responses of ChatGPT when prompted with free text mostly contain a
single numeric rating, which is the same behavior when it is instructed by
score only. This means that what the model is allowed to generate is more
important than what it really generates.

The above observations make us curious if the correlations can be higher if
ChatGPT is instructed to justify its ratings. Inspired by chain-of-thought in
Wei et al. (2022b) and Kojima et al. (2022) (not the auto CoT in 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-rate is more like the zero-shot chain-of-thought
(Kojima et al., 2022). Refer to Appendix F.1.1 for the exact prompts we use.

The results of asking ChatGPT to explain/analyze how they rate the sample are
shown in the last two rows in Table 1 and Appendix Table 2. We find that for
all attributes of both datasets, rate-explain and anlyze-rate both lead to
correlations stronger than or at least comparable to the correlation of asking
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/analyze when rating, ChatGPT’s correlation can be better than or
comparable to the state-of-the-art correlation coefficients obtained from
GPT-4 in Liu et al. (2023) for coherence of SummEval and three attributes of
Topical-Chat. We hypothesize that some attributes (e.g., coherence for
SummEval) are harder for ChatGPT to rate, so the correlations for these
attributes show a larger improvement when ChatGPT explains how it rates the
sample.

In rate-explain, the output of ChatGPT contains a 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 on 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 it needs to explain the ratings, it tends to generate
ratings that are easier for it to explain, and a rating that is more aligned
to humans’ rating is easier for ChatGPT to explain.

###  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 is
hard to determine which one to use. A valid method is sampling some ratings
using rate-explain and sampling some ratings using analyze-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 to explain; this result is shown in
Appendix Table 3.

###  3.4 Robustness of the Guidelines

LLMs are notorious for their performance fluctuation 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 conduct 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.

####  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 temperatures: $0.7$ and $0.3$.

The results are shown in Appendix Table 5 and summarized as follows: First,
when fixing the sampling temperature, we find that rate-explain and analyze-
rate always achieve a higher correlation compared with G-Eval. This supports
our guideline that "asking the LLM to explain/analyze outperforms 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 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 are more robust than G-Eval with respect to the
sampling temperature.

####  3.4.2 Changing the Prompts

We 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. 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, honest, and helpful. The human annotator prompt is inspired by
Chiang and Lee (2023), who use a similar prompt to make the LLM behave as a
human annotator. These two prompts will be inserted before 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 different when prompted with a different persona. The prompts
are detailed in Appendix F.3.

The results are shown in Table 6 and summarized as follows: rate-explain and
analyze-rate consistently 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 correlations 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 human-annotator prompt and the HHH
prompt. This shows that asking the LLM to explain is more robust to the
variation of the prompts.

##  4 Conclusion

We study how to better use ChatGPT as an automatic evaluation tool by
scrutinizing LLM evaluation and G-Eval. We provide concrete guidelines and
show that by using those guidelines, the correlations of several evaluated
attributes given by ChatGPT, a publicly usable model, can be higher than or
comparable to the ratings given by GPT-4, a highly restricted and pricey
model. We also show that the evaluation results based on 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 evaluation.

## Limitations

There are three main limitations of this paper.

  1. 1.

We only use ChatGPT to conduct the experiments in this paper. We explain why
we chose ChatGPT in Section 2.3. We believe that using ChatGPT is already
enough since we show that the correlations obtained by using ChatGPT are
already comparable to or better than the previous SoTA results obtained by
GPT-4.

  2. 2.

We only conduct analysis using two tasks, while we know that NLP has more
diverse tasks. We do not guarantee that our observations can generalize to all
the other datasets. We recommend the users verify the effectiveness of using
LLM to evaluate the tasks of interest.

  3. 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.

## Ethics Statement

Our paper follows the ACL Code of Ethics. We do not see a particular harmful
outcome of our paper. The code and datasets for reproducing our experiments
can be found at https://github.com/d223302/A-Closer-Look-To-LLM-Evaluation/.

## Acknowledgements

We want to thank the reviews for providing detailed feedback and actionable
suggestions, which helped us strengthen our paper. We also want to thank the
senior committee members for monitoring the reviewing process. Cheng-Han
Chiang is supported by a Ph.D. scholarship program by Delta Electronics.

## References

  * Almazrouei et al. (2023) Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023.  Falcon-40B: an open large language model with state-of-the-art performance.
  * Askell et al. (2021) Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al. 2021.  A general language assistant as a laboratory for alignment.  _arXiv preprint arXiv:2112.00861_.
  * Bai et al. (2022) 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 Hatfield-Dodds, 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 helpful and harmless assistant with reinforcement learning from human feedback.
  * Bojar et al. (2017) Ondřej Bojar, Yvette Graham, and Amir Kamran. 2017.  Results of the WMT17 metrics shared task.  In _Proceedings of the Second Conference on Machine Translation_ , pages 489–513, Copenhagen, Denmark. Association for Computational Linguistics.
  * Chiang and Lee (2023) Cheng-Han Chiang and Hung-yi Lee. 2023.  Can large language models be an alternative to human evaluations? In _Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 15607–15631, Toronto, Canada. Association for Computational Linguistics.
  * Fabbri et al. (2021) Alexander R Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021.  Summeval: Re-evaluating summarization evaluation.  _Transactions of the Association for Computational Linguistics_ , 9:391–409.
  * Gopalakrishnan et al. (2019) Karthik Gopalakrishnan, Behnam Hedayatnia, Qinlang Chen, Anna Gottardi, Sanjeev Kwatra, Anushree Venkatesh, Raefer Gabriel, and Dilek Hakkani-Tür. 2019.  Topical-chat: Towards knowledge-grounded open-domain conversations.
  * Graham and Baldwin (2014) 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.
  * Graham et al. (2015) Yvette Graham, Timothy Baldwin, and Nitika Mathur. 2015.  Accurate evaluation of segment-level machine translation metrics.  In _Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_ , pages 1183–1191, Denver, Colorado. Association for Computational Linguistics.
  * Hermann et al. (2015) Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015.  Teaching machines to read and comprehend.  _Advances in neural information processing systems_ , 28.
  * Huang et al. (2023) Fan Huang, Haewoon Kwak, and Jisun An. 2023.  Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech.  _arXiv preprint arXiv:2302.07736_.
  * Kojima et al. (2022) Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022.  Large language models are zero-shot reasoners.  In _Advances in Neural Information Processing Systems_.
  * Liu et al. (2023) Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023\.  Gpteval: Nlg evaluation using gpt-4 with better human alignment.  _arXiv preprint arXiv:2303.16634_.
  * Macháček and Bojar (2014) Matouš Macháček and Ondřej Bojar. 2014.  Results of the WMT14 metrics shared task.  In _Proceedings of the Ninth Workshop on Statistical Machine Translation_ , pages 293–301, Baltimore, Maryland, USA. Association for Computational Linguistics.
  * Mehri and Eskenazi (2020) 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.
  * OpenAI (2022) OpenAI. 2022.  Chatgpt: Optimizing language models for dialogue.  Accessed on January 10, 2023.
  * OpenAI (2023) OpenAI. 2023.  Gpt-4 technical report.
  * Ouyang et al. (2022) 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 instructions with human feedback.  _Advances in Neural Information Processing Systems_ , 35:27730–27744.
  * Sanh et al. (2022) 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 Szczechla, 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, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. 2022.  Multitask prompted training enables zero-shot task generalization.  In _International Conference on Learning Representations_.
  * Wang et al. (2023) Jiaan Wang, Yunlong Liang, Fandong Meng, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, and Jie Zhou. 2023.  Is chatgpt a good nlg evaluator? a preliminary study.  _arXiv preprint arXiv:2303.04048_.
  * Wei et al. (2022a) Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. 2022a.  Finetuned language models are zero-shot learners.  In _International Conference on Learning Representations_.
  * Wei et al. (2022b) 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 prompting elicits reasoning in large language models.  In _Advances in Neural Information Processing Systems_.
  * Zeng et al. (2023) Andy Zeng, Maria Attarian, brian ichter, Krzysztof Marcin Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael S Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, and Pete Florence. 2023.  Socratic models: Composing zero-shot multimodal reasoning with language.  In _The Eleventh International Conference on Learning Representations_.
  * Zhang* et al. (2020) Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020.  Bertscore: Evaluating text generation with bert.  In _International Conference on Learning Representations_.
  * Zhong et al. (2022) 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 _Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing_ , pages 2023–2038, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

##  Appendix A Why We Cannot Fairly Compare with the Results in Liu et al.
(2023)

As a work highly related to G-Eval, we would really like to compare our
results with G-Eval. However, we encounter difficulties when comparing our
results with those in Liu et al. (2023) for the following reasons.

  * •

G-Eval proposes to use GPT-4 as the evaluation tool, while it is currently a
highly restricted model, and we only have limited access to it.

  * •

G-Eval only releases the prompts for SummEval. We need to construct the
prompts for Topical-Chat based on the human evaluation instructions released
by Mehri and Eskenazi (2020). It 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.

  * •

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 explanations. This makes us unable to directly compare our
results with the results in Liu et al. (2023).

  * •

We cannot reproduce the numbers on the paper of G-Eval even when using their
official implementation and the GPT-4 responses they release. This means that
the only thing we do is calculate the correlation coefficient using 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 paper. Moreover, the results of fluency
they provide is the result not using auto CoT, but the results of the other
three attributes for SummEval use auto CoT. That is why we use a question mark
for the auto CoT field in Table 1.

  * •

The Table 2 in Liu et al. (2023) seems to be wrong. The caption (Spearman’s
$\rho$ and Kendall’s $\tau$) does not match the headers ($r$ and $\rho$). This
makes us hard to compare their results with ours reliably.

##  Appendix B Supplementary Results for Topical-Chat

Table 2 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 $r$ and Kendall’s $\tau$ are calculated in Appendix C.

###  B.1 Is Auto CoT Useful When ChatGPT Is Asked to Explain?

In Table 3, we show the results when we add the evaluation steps generated by
auto CoT when we ask ChatGPT when prompting with (rate-explain). We find that
on groundedness, using auto CoT is worse. However, for the other three
attributes, using auto CoT is better. This again shows that auto CoT is not
particularly useful.

Sec. | Ablations | Naturalness | Coherence | Engagingness | Groundedness
---|---|---|---|---|---
CoT | Output | $r$ | $\tau$ | $r$ | $\tau$ | $r$ | $\tau$ | $r$ | $\tau$
| ✗ | Score only | 0.393 | 0.358 | 0.468 | 0.391 | 0.549 | 0.513 | 0.311 | 0.566
| ✓ | rate-explain | 0.554 | 0.478 | 0.512 | 0.429 | 0.613 | 0.566 | 0.555 | 0.664
3.2 | ✗ | rate-explain | 0.524 | 0.47 | 0.477 | 0.416 | 0.567 | 0.524 | 0.58 | 0.693
Table 3: The Pearson’s $r$ and Kendall’s $\tau$ correlation coefficient
between LLMs’ ratings and human ratings for Topical-Chat. All the results in
this table, except the first row, are from ChatGPT. We boldface the Pearson’s
$r$ statistically significantly higher than auto CoT + score only. We
underline the Pearson’s $r$ comparable auto CoT + score only.

##  Appendix C Calculation of Correlation Coefficient

In this paper, we calculate Pearson’s $r$ and Kendall’s $\tau$ between human
ratings and ChatGPT’s ratings. Whether to use Spearman’s rank correlation or
Pearson’s (linear) correlation to evaluate the alignment between human ratings
and an automatic evaluation metric is long-standing, but there has been an
increasing trend towards Pearson’s correlation since 2014 (Macháček and Bojar,
2014; Graham and Baldwin, 2014; Zhang* et al., 2020). We use the pearsonr and
kendalltau in scipy.stats for calculating the correlation coefficients. For
each attribute of each sample, the rating of ChatGPT is obtained by 20
samples; we set the decoding temperature to $1$ and the top-$p$ in nucleus
sampling to $1$, following G-Eval (Liu et al., 2023).

Consider a dataset with $N$ source documents, and each source document has $M$
corresponding target documents. We also have the human ratings for $N\cdot M$
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 coefficient. So the $N\cdot M$ ratings are the
average ratings from different raters. In the case of SummEval, we have
$N=100$ source documents and $M=16$ summaries generated by 16 summarization
models. There are two different methods for calculating correlation
coefficients.

####  C.0.1 Method 1: Dataset-Level Correlation Coefficient

In this method, we first obtain the ratings on $N\cdot M$ target documents
from ChatGPT. We then calculate the correlation coefficient between the
$N\cdot M$ ChatGPT’s ratings and the $N\cdot M$ average human ratings. In this
case, the correlation coefficient is calculated among two $N\cdot M$ vectors,
meaning that the correlation coefficient is calculated across the entire
dataset.

####  C.0.2 Method 2: Document-Level Correlation Coefficient

In this method, for each source document, we obtain the ratings of its $M$
target documents using ChatGPT. Next, we calculate the correlation coefficient
between these $M$ ChatGPT ratings and the corresponding $M$ human ratings.
After iterating the above process over all the $N$ source documents, we obtain
the $N$ correlation coefficients. We average the $N$ correlation coefficients
as the final correlation coefficient. In this case, the correlation
coefficient is calculated at the document-level and averaged over the whole
dataset.

###  C.1 How We Calculate the Correlation Coefficient

In Table 1 and 2 in this paper, we use Method 1 (Subsection C.0.1) to
calculate Pearson’s correlation, following the recommendation in Graham et al.
(2015). Calculating the correlation coefficient on the dataset level is also
used in LLM evaluation (Chiang and Lee, 2023). Calculating a single
correlation coefficient on the dataset level allows us to use William’s test
to test whether two Pearson’s $r$ are significantly different.

For Kendall’s $\tau$ in Table 1 and 2, we follow most prior works (Zhong et
al., 2022; Liu et al., 2023) to calculate Kendall’s $\tau$ using Method 2
(document-level, Section C.0.2) to understand if ChatGPT can differentiate the
quality difference between different system outputs for the same source
document.

In fact, we find that Pearson’s $r$ calculated by Method 1 and Method 2 are
highly correlated. In Table 4, we show the result of Topical-Chat while we use
Method 2 to calculate Pearson’s $r$; Kendall’s $\tau$ is still calculated by
Method 2. Comparing the results of Pearson’s $r$ in Table 2 and Table 4, one
can easily see that when a method have significantly higher Pearson’s $r$ in
Table 2, it will also have significantly higher Pearson’s $r$. We present the
$r$ calculated by Method 1 because it makes more sense when calculating
statistical significance when the correlation coefficient is calculated at the
dataset-level (Graham et al., 2015).

Sec. | Ablations | Naturalness | Coherence | Engagingness | Groundedness
---|---|---|---|---|---
CoT | Output | $r$ | $\tau$ | $r$ | $\tau$ | $r$ | $\tau$ | $r$ | $\tau$
GPT-4† | ✓ | Score only | 0.549 | - | 0.594 | - | 0.627 | - | 0.531 | -
| ✓ | Score only | 0.445 | 0.358 | 0.498 | 0.391 | 0.579 | 0.513 | 0.685 | 0.566
3.1 | ✗ | 0.431 | 0.331 | 0.507 | 0.404 | 0.631 | 0.535 | 0.666 | 0.582
| ✗ | Score only | 0.431 | 0.331 | 0.507 | 0.404 | 0.631 | 0.535 | 0.666 | 0.582
| ✗ | Free Text | 0.572 | 0.476 | 0.523 | 0.426 | 0.676 | 0.557 | 0.747 | 0.666
| ✗ | Rate-explain | 0.621 | 0.512 | 0.472 | 0.425 | 0.61 | 0.509 | 0.771 | 0.663
3.2 | ✗ | Analyze-rate | 0.573 | 0.47 | 0.486 | 0.416 | 0.628 | 0.524 | 0.725 | 0.693
Table 4: The Pearson’s $r$ and Kendall’s $\tau$ correlation coefficient
between LLMs’ ratings and human ratings for Topical-Chat. Note that in this
table, both Pearson’s $r$ and Kendall’s $\tau$ are calculated by Method 2 in
Appendix C.0.2. All the results in this table, except the first row, are from
ChatGPT. The results of GPT-4 are from Liu et 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.

##  Appendix D Results of Changing the Temperature and Prompts

We show the results of varying the temperature used to sample the ChatGPT
output in Table 5. In the experiments in this section, we only sample $N=5$
samples from the ChatGPT since we find that G-eval and our proposed guidelines
are quite robust to the number of samples when $N\geq 5$.

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
✗ | Rate-explain | 0.526 | 0.468 | 0.414 | 0.485
✗ | Analyze-rate | 0.605 | 0.448 | 0.441 | 0.392
(b) Temperature $T=0.7$

Auto-CoT | Output | Coherence | Consistency | Fluency | Relevance
---|---|---|---|---|---
✓ | Score only | 0.450 | 0.370 | 0.319 | 0.403
✗ | Rate-explain | 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 in Table 1)

Table 5: Comparing G-Eval (Auto-CoT + score only) with rate-explain and
analyze-rate at different temperatures. We boldface Pearson’s r 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
✗ | Rate-explain | 0.596 | 0.465 | 0.403 | 0.478
✗ | Analyze-rate | 0.596 | 0.493 | 0.475 | 0.406
(b) Results when prompted with the 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 r
statistically significantly higher than the baseline (the first row in each
subtable).

##  Appendix E Datasets

###  E.1 SummEval

SummEval (Fabbri et al., 2021) is a dataset for the meta-evaluation of
summarization. It contains 100 source documents, each with 16 summaries
obtained 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, consistency, fluency of the summary, and relevance between the
summary and the source document. Each attribute is rated based on a 5-point
Likert scale. We download the source documents, summaries, and human ratings
from the GitHub repository of G-Eval
(https://github.com/nlpyang/geval/tree/8f54105/data). SummEval was released
under MIT License, and our usage for research does not violate the dataset’s
initial intention.

###  E.2 Topical-Chat

Topical-Chat (Gopalakrishnan et al., 2019) is a knowledge-grounded open-domain
dialogue dataset. The dataset consists of a dialogue context (history), an
interesting fact related to the topic of the conversation, and a response.
Mehri and Eskenazi (2020) releases high-quality human annotations on 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 a total of 360 dialogue-response pairs. Those pairs are evaluated
based on six attributes, and we follow Zhong et al. (2022) and Liu et al.
(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://github.com/maszhongming/UniEval/blob/main/reproduce/data/dialogue/topical_chat.json.

##  Appendix F Prompts

We list the prompts we use in this section. In the main content of the paper
and in the following parts, we use different highlight colors to represent
different parts of the prompt. A prompt is composed of four parts: (1) 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) a sentence used to
prompt the LLM to give the rating.

The prompts for different attributes of the same dataset share the same
descriptions of the rating task. Different attributes use different definition
and rating criteria. In G-Eval, the prompts also compose of the evaluation
steps generated by auto CoT.

###  F.1 Prompts for SummEval

The descriptions of the rating task, the definition and rating criteria, the
evaluation steps for coherence, consistency, and relevance in SummEval is from
the prompts released by G-Eval in their GitHub repository
(https://github.com/nlpyang/geval/tree/8f54105/prompts/summeval). 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 to
rate fluency on a scale of 1 to 3
(https://github.com/nlpyang/geval/blob/8f54105061e00377fbb909153892d5bfb5b3623a/prompts/summeval/flu_detailed.txt),
while the original rating scale in SummEval is 1 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 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 in the human evaluation, as
emphasized in Chiang and Lee (2023).

First, we show an example prompt for coherence. This prompt corresponds to the
score only + auto CoT in Table 1.

##### 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 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."
Evaluation Steps:
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 news article, and if it presents
them in a clear and logical order.
3\. Assign a score for coherence on a scale of 1 to 5, where 1 is the lowest
and 5 is the highest based on the Evaluation Criteria.
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 1 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, free text, rate-explain, and analyze-rate. The prompts for
free text is attribute-dependent, and we list them in the Their corresponding
output prompts are listed as follows:

##### Score only

Evaluation Form (scores ONLY):
\- {Attribute}:

##### Rate-explain

Evaluation Form (Answer by starting with "Rating:" and then give the
explanation of the rating on the next line by "Rationale:"):
\- {Attribute}:

##### Analyze-rate

Evaluation Form (Answer by starting with "Analysis:" to analyze the given
example regarding the evaluation criteria as concise as possible, and then
give the numeric rating on the next line by "Rating:):
\- {Attribute}:

####  F.1.2 Attribute-Dependent Prompts

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
attribute-dependent, and we list them as follows. We use different colors to
denote different parts in the prompt. Note that the following prompts are not
the complete prompts used as the model input; they need to be used with the
descriptions of the rating task and the sample to be rated.

##### Coherence

Evaluation Criteria:
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-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."

Evaluation Steps:
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 news article, and if it presents
them in a clear and logical order.
3\. Assign a score for coherence on a scale of 1 to 5, where 1 is the lowest
and 5 is the highest based on the Evaluation Criteria.

Question:
How coherent is the summary? That is, how well do the sentences in the summary
fit together? (On a scale of 1-5, with 1 being the lowest)

##### Consistency

Evaluation Criteria:
Consistency (1-5) - the factual alignment between the summary and the
summarized source. A factually consistent summary contains only statements
that are entailed by the source document. Annotators were also asked to
penalize summaries that contained hallucinated facts.

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 supported by the article.
3\. Assign a score for consistency based on the Evaluation Criteria.

Question:
How consistent is the summary with the source document in terms of the factual
alignment? (On a scale of 1-5, with 1 being the lowest)

##### Fluency

Evaluation Criteria:
Fluency (1-5): This rating measures the quality of individual sentences, are
they well-written and grammatically correct. Consider the quality of
individual sentences.

Evaluation steps:
1\. Read the given summary.
2\. Evaluate the fluency of the summary on a scale of 1-5 based on the
criteria provided.
3\. Provide the rating.

Question:
Based on the evaluation criteria, how fluent is the summary? (On a scale of
1-5, with 1 being the lowest)

##### Relevance

Evaluation Criteria:
Relevance (1-5) - selection of important content from the source. The summary
should include only important information from the source document. Annotators
were instructed to penalize summaries which contained redundancies and excess
information.

Evaluation Steps:
1\. Read the summary and the source document carefully.
2\. Compare the summary to the source document and identify the main points of
the article.
3\. Assess how well the summary covers the main points of the article, and how
much irrelevant or redundant information it contains.
4\. Assign a relevance score from 1 to 5.

Question:
On a scale of 1-5, with 1 being the lowest, is the summary relevant to the
source document and does the summary only contain the important information of
the source document?

###  F.2 Prompts for Topical-Chat

First, we show an example prompt for naturalness. This prompt corresponds to
the score only + auto CoT in Table 2.

##### Naturalness

You will be given a 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.
Your task is to rate the responses 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 Crieteria:
Naturalness (1-3) Is the response naturally written??
\- A score of 1 (bad) means that the response is unnatural.
\- A score of 2 (ok) means the response is strange, but not entirely
unnatural.
\- A score of 3 (good) means that the response is natural.

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 on its naturalness, using the provided
criteria.
4\. Assign a rating score of 1, 2, or 3 based on the evaluation.

Example:
Conversation History:
{{Document}}
Corresponding Fact:
{{Fact}}
Response:
{{Response}}

Evaluation Form (scores ONLY):
\- Naturalness:

####  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 we use can be found in the supplementary data of this
paper.

####  F.2.2 Attribute-Dependent Prompts

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
attribute-dependent, and we list them as follows. Again, the following prompts
are not the complete prompts used as the model input; they need to be used
with the descriptions of the rating task and the sample to be rated.

##### Naturalness

Evaluation Crieteria:
Naturalness (1-3) Is the response naturally written??
\- A score of 1 (bad) means that the response is unnatural.
\- A score of 2 (ok) means the response is strange, but not entirely
unnatural.
\- A score of 3 (good) means that the response is natural.

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 on its naturalness, using the provided
criteria.
4\. Assign a rating score of 1, 2, or 3 based on the evaluation.

Question:
How natural is the reponse? (On a scale of 1-3, with 1 being the lowest)

##### Coherence

Evaluation Crieteria:
Coherence (1-3) Does the response serve as a valid continuation of the
conversation history?
\- A score of 1 (no) means that the response drastically changes topic or
ignores the conversation history.
\- A score of 2 (somewhat) means the response refers to the conversation
history in a limited capacity (e.g., in a generic way) and shifts the
conversation topic.
\- A score of 3 (yes) means the response is on topic and strongly acknowledges
the conversation history.

Evaluation Steps:
1\. Read the conversation history.
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 3 for coherence.

Question:
Does the response serve as a valid continuation of the conversation history?
(On a scale of 1-3, with 1 meaning the response is invalid and 3 meaning the
response is coherent)

##### Engagingness

Evaluation Crieteria:
Engagingness (1-3) Is the response dull/interesting?
\- A score of 1 (dull) means that the response is generic and dull.
\- A score of 2 (somewhat interesting) means the response is somewhat
interesting and could engage you in the conversation (e.g., an opinion,
thought)
\- A score of 3 (interesting) means the response is very interesting or
presents an interesting fact

Evaluation Steps:
1\. Read the conversation, the corresponding fact and the response carefully.
2\. Rate the response on a scale of 1-3 for engagingness, according to the
criteria above.

Question:
Is the response interesting and engaging? (On a scale of 1-3, with 1 meaning
dull and 3 meaning interesting)

##### Groundedness

Evaluation Crieteria:
Groundedness (0-1) given the fact that this response is conditioned on,
determine whether this response uses that fact.
\- A score of 0 (no) means the response does not mention or refer to the fact
at all
\- A score of 1 (yes) means the response uses the fact well

Evaluation Steps:
1\. Read the conversation between the two 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 a score of 0 or 1 for groundedness 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 a scale of 0-1, with 0 meaning no and 1 meaning yes)

###  F.3 Prompts for Section 3.4.2

##### HHH prompts

You are an AI assistant. The AI tries to be helpful, polite, honest,
sophisticated, emotionally aware, and humble-but-knowledgeable. 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.