Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT
Authors: Biru Zhu
Lifan Yuan
Ganqu Cui
Yangyi Chen
Chong Fu
Bingxiang He
Yangdong Deng
Zhiyuan Liu
Maosong Sun
Ming Gu
Venue: EMNLP 2023 Main (2023)
Keywords: LLM-generated text detection
large language model

Abstract: Large language models (LLMs), e.g., ChatGPT, have revolutionized the domain of natural language processing because of their excellent performance on various tasks. Despite their great potential, LLMs also incur serious concerns as they are likely to be misused. There are already reported cases of academic cheating by using LLMs. Thus, it is a pressing problem to identify LLM-generated texts. In this work, we design a zero-shot black-box method for detecting LLM-generated texts. The key idea is to revise the text to be detected using the ChatGPT model. Our method is based on the intuition that the ChatGPT model will make fewer revisions to LLM-generated texts than it does to human-written texts, because the texts generated by LLMs are more in accord with the generation logic and statistical patterns learned by LLMs like ChatGPT. Thus, if the text to be detected and its ChatGPT-revised version have a higher degree of similarity, the text is more likely to be LLM-generated. Extensive experiments on various datasets and tasks show that our method can effectively detect LLM-generated texts. Moreover, compared with other detection methods, our method has better generalization ability and is more stable across various datasets. The codes are publicly available at https://github.com/thunlp/LLM-generated-text-detection.

Beat LLMs at Their Own Game:
Zero-Shot LLM-GGeerated Text Detection via Querying ChatGPT
Biru Zhu', Lifan Yuan?; Ganqu Cui Yangyi Chen², Chong Fu', Bingxiang He² Yangdong Deng Zhiyuan Liu Maosong Sun?, Ming Gu-
School of Software, Tsinghua University, China
2 Department of Computer Science and Technology, Tsinghua University, China
3 University of Illinois Urbana- ChammaaCh USA Zhejiang University, China zbr 90mails. tsinghua edu. cn, lievanyuan1 (arrryuuuuun3eemalaa 33gmail.. com {dengyd, liuzy)@tsinghua. edu. cn
Abstract
The strong emergent abilities, however, also trig- ger social concerns for the misuse of LLMs. The academic cheating by using LLMs to write essays and homework has been repeatedly reported. The fabricated news generated by LLMs also causes significant problem to our society. Thus, it is an important problem to detect whether a piece of text is generated by the LLM. Following pre- vious works (Jawahar et al., 2020; Mitchell et al., 2023), we formulate the problem of detecting LLM- generated texts as a binary classification task, i.e.. classifying whether a piece of text is generated by the LLM or written by human.
Large language models (LLMs). e.g.. ChatGPT, have revolutionized the domain of natural lan- guage processing because of their excellent per- formance on various tasks. Despite their great potential, LLMs also incur serious concerns they likely to be misused. There are al- ready reported cases of academic cheating by using LLMs. Thus, it is a pressing problem to identify L.L--genertedd texts. In this work, we design a zero-shot Mox method for detecting I.L.-generatel texts. The key idea is to revise the text to be detected using the ChatGPT model. Our method is based on intuition that the ChatGPT model will make fewer revisions to ALM-generated texts than it does to human- written texts, because the texts generated by LLMs are more in accord with the generation logic and statistical patterns learned by LLMs like ChatGPT. Thus, if the to be detected and its ChatGPT-revised version have higher degree similarity, the text is more likely be LLM-generated. Extensive experiments on various datasets and tasks show that our method can effectively detect L.LM- generated texts. Moreover, compared with other detection methods, our method has better generalization ability and more stable across various datasets. The codes are publicly avail- able at ht https: //gittub........ com/ thunlp/LLM-gene rated- text-detection.
Previous methods for detecting LLM- MeMegeerrttedd texts can be classified into two categories. The first category is the zero-shot detection method (So- laiman et al., 2019; Gehrmann et al., 2019; Mitchell et al., 2023), which needs access the model's out- put logits losses for detection. However, many LLM services provided by commercial companies do not expose the model's output logits or losses at the inference time. Thus, these methods have to rely on local proxy models to access the output information. However, the inconsistency between the online model and the local proxy model may lead to poor detection performance. The second category the supervised fine-tuning method (Guo et al., 2023), which trains a Deep Neural Network (DNNN-based classifier with the labeled training data. It is prone to overfitting the training data and thus may have poor generalization ability.
1 Introduction
Benefiting from the emergent ability, large lan- guage models (LLMs) have demonstrated excellent performance on a large number of natural language processing tasks (Brown et al., 2020; Sanh et al., 2022). Recently, LLMs (e.g., ChatGPT) trained to follow instructions (Ouyang et al., 2022; Wei et al., 2022) generate high-quality responses when given specific instructions and input data by user.
To address the abovementioned problems, we propose a zero-shot black-box detection method leveraging the ChatGPT model. Our method is based on the following finding. If we use the Chat- GPT model to revise texts, it will make fewer revi- sions to LLM-ggnnrated texts than it does human- written texts. The underlying reason may be that the --geerred texts conform to the genera- tion logic and statistical patterns learned by the ChatGPT model. The overall framework of our
LLifan Yuan has graduated from Huazhong University Science Technology and he is doing an internship THUNLP group now.
Corresponning author.

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Given the text 2,
lems. Many applications are built upon them, e.g.. chatbots. Though LLMs act as useful tools, mis- using them may cause bad influence, e.g. students use LLMs to do their homework.
(1) Add the prompt
"Revise the following
(2) Revise
ChatGPT
Revisc
LLM-Generated Text Detection. Previous LLM-generated text detection methods can generally be classified into two categories. The first category is the zero-shot detection method (So- laiman et al., 2019; Gehrmann et al., 2019; Mitchell et al., 2023). However, these methods rely on the model's 's output logits or losses. If there is access to the output logits or losses of the source model that generates the text be detected, these methods can use proxy model to get the proxy model's output logits or losses for detection. However, the difference between the source model and the proxy model may cause poor detection performance. Thus, it is essential to design a detection method without accessing the model's output logits or losses.
Revised text T
(3) Score
Similarity Metric Sim Sim (x...t))
(4) Compare
Similarity Score &, 2E ?
I, generated by LLM
2 z; written by human
The overall framework of our approach. Firstly, the text to be detected is revised by Chat- GPT. Then our method calculates the similarity score s; between the text 2; and its corresponding revised text Ti. If the similarity score is no smaller than the threshold e, the text x, is likely to be generated by LLM.
Figure 1:
method is shown in Figure 1. Firstly, we leverage the ChatGPT model to revise the text to be detected. Then measure the similarity between the text to be detected and its ChatGPT-revised version using the similarity metric. The applied similarity met- ric could be BLEU score (Papineni et al., 2002), ROUGE score (Lin, 2004), BERTScore (Zhang* al., 2020) or BARTScore (Yuan et al., 2021). The LLM-generated texts are more likely to achieve higher similarity scores compared with human- written texts.
The second category is to train a DNN- based classifier using some labeled human- and L.LM-generted samples (Guo et al., 2023; Uchendu et al., 2020). However, it is hard to col- lect sufficient labeled training samples to train a generalizable DNN-based classifier (Bakhtin et al.. 2019; Uchendu et al., 2020). Moreover, training DNN- based classifiers are vulnerable backdoor attacks (Qi et al., 2021) and the ONN--ased detec- tor is lack of interpretability. Thus, it is important to build an explainable detector without training DNN-based classifiers.
We perform experiments on six datasets includ- ing question answering and summarization genera- tion tasks. The experimental results prove that our method can effectively detect LLM-generated texts. Besides, we compare our method with other zero- shot detection methods and the supervised fine- tuning method. The experimental results show that our detection method is more universal across var- ious datasets and has better generalization ability. Moreover, our method is robust to dataset biases.
Jawahar al. (2020) survey some automatic de- tection methods and identify some future directions for building useful detectors. He et al. (2023) de- velop a benchmark for evaluating existing detection methods and call for more robust detection meth- ods. In this paper, we aim to design a robust and explainable black-box detection method without training DNN-based classifiers.
2 Related Work
Method
Large Language Models. Large language mod- els (LLMs) have achieved fantastic performance on a huge number of natural language processing tasks (Brown et al., 2020). Some recent works show that LLMs can be trained to follow instruc- tions with instruction-tuning (Ouyang et al., 2022; Wei et al., 2022; Chung al., 2022). LLMs pro- vided by AI companies like OpenAI can generate responses to users' inputs and solve different prob-
In this section, we illustrate our method step by step. (1) Given the texts (21, (2,,,2,,..,,,, to be detected, first revise the texts using the ChatGPT model. Specifically, we add the prompt, i.e., "Revise the following text: ". before the texts to be detected. Then we feed the modified texts into the ChatGPT model M and use its responses {ū1, T2, .... Zn as the revised texts.

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collected by Guo et al. (2023), For more details of the datasets, please refer to appendix D. We also demonstrate that our method can detect texts that are generated by other source models in section 4.3.
T - Mr the following text: + xi),
i E (1,
(1)
(2) Then we use an unsupervised similarity metric Sim to calculate the similarity score 8; between the original text x; and its revised text ž;
4.2
Main Experiments
4.2.1
Comparisons Zero-Shot Setting
9: Sim (I,, 5;:)
(2)
Zero-Shot Methods for Comparison. We com- pare our method with other zero-shot detec- tion methods including Log-Likelihood (Solaiman et al., 2019), Rank (Gehrmann et al., 2019), Log- Rank (Mitchell et al., 2023), Entropy (Gehrmann et al., 2019) and DetectGPT (Mitchell et al., 2023). These zero-shot detection methods are based on dif- ferences between output losses/logits of the model on Homman-written texts and LLM-generated texts. They consider that the model will be more famil- iar with LLM-generated texts. For example, the Log-Likelihood method takes the negative loss of the model on the text to be detected as the Log- Log-Likelihood score. A higher Log-Likelihood score indicates the text is more likely to be LLM- generated. The details of these detection methods are shown in appendix C. Since the logits or losses of ChatGPT can not be accessed at the inference time, we use GPT-2-medium (Radford et al., 2019) as the proxy model for deriving the logits or losses for these methods, following He al. (2023). Metrics. Following Mitchell et al. (2023), we use the AUROC the evaluation metric. For our method, we use the BARTScore- (Yuan et al., 2021) the similarity metric. We also prove that our method is effective under different similarity metrics. For more details of our method's detec- tion performance when using different similarity metrics including BLEU, ROUGE and BERTScore, please refer to appendix A.
We take the BARTScore as an example. Given a eqquence-o--eqqunne pre-trained model like BART model (Lewis et al., 2020) which is pa- rameterized by 0, the BART is calculated using the log probability of the target text z; given the revised text ã; the source text. The target text is tokenized into sequence of tokens: 2, [ F22, ... R.
BARTScore logp (1)())))))) 2s,
The BARTScore can measure the semantic cov- erage between the source text and the target text (Yuan et al., 2021). For the calculation of BARTScore in this paper. we use the BARTScore- CNN. which uses BART model that is fine-tuned on the CNNDM dataset (Hermann et al., 2015). For more details of other evaluated similarity metrics, please refer appendix B. The L.L--geeeated text and its revised text are more similar compared with the Human--ritter text and its revised text. Thus, a higher similarity score s; indicates that the text z; is more likely to be LLM--geerated.
4 Experiments
In this section, evaluate the detection perfor- mance of our method and compare our method with other detection methods.
Results. As shown in Table our method achieves good detection performance across various datasets. Specifically, the AUROC is consistently higher than 70% for our method on all datasets. The av- erage AUROC on all datasets is 90.05% for our method. However, for other zero-shot detection methods, the AUROC is low on MultiNews, Gov- Report and BillSum datasets. The reason may be that the training data of ChatGPT is different from that of GPT-2-medium. Thus, the GPT-2-medium model may be unfamiliar with some ChatGPT- generated texts, reflected in the high losses on some ChatGPT MenGeP-ggeeeated texts. As a result, the detec-
4.1 Experimental Setting
Datasets. For the summarization generation task, we perform experiments on MultiNews (Fabbri et al., 2019), GovReport (Huang et al., 2021) and BillSum (Kornilova and Eidelman, 2019) datasets. We use the representative LLM, i.e., ChatGPT (gpt- 3.5-turbo), as the source model to generate sum- maries be detected. For the question answer- ing task, we consider three datasets including Fi- nance (Maia et al., 2018), Medicine (Chen al., 2020), and Reddit Eli5 (Fan et al., 2019). In main experiments, we use the ChatGPT-generater texts

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Method
Finance
Medicine
Reddit Elis
MultiNews
GovReport
BillSum
Average Value
Log-Likelihood
98.61
98.85
99.08
55.07
36.17
34.15
70.32
Rank
92.24
97.62
78.81
$6.61
42.44
37.03
67.46
Log-Rank
98.64
98.91
99.26
56.12
36.93
35.79
70.94
Entropy
97.07
98.79
97.78
52.31
31.54
24.50
67.0
DetectGPT
88.56
96.43
83.66
40.36
43.63
36.57
64.87
Our Method (BARTSCcre-CNNN)
97.40
95.06
99.15
86.28
75.64
86.77
90.05
Table Comparisons with other zero-shot detection methods. The evaluation metric is AUROC (%). The "Average Value" is the average performance all datasets.
Source Dataset Fine-Tuning
Our Method
ity score and the maximum similarity score into 10,000 uniform intervals. We get 10,000 interpola- tion values between the minimum similarity score and the maximum similarity score. Finally, we use the best interpolation value with the highest classification accuracy the source dataset the optimal threshold.
Finance
81.77
80.05
Medicine
67.35
80.62
Reddit EliS
52.68
69.93
MultiNews
51.21
83.44
GovReport 83.62
82.67
BillSum 65.03
81.39
Table 2: The average accuracy (%) on all target datasets the fine-tuning method and our method in the OOD setting when using original datasets source datasets.
We find that the fine-tuming tinettuning method performs well in the situation where the distributions of the training and testing data are identical, as shown in appendix A. However, in practice, it is difficult to guarantee that the distributions of the training and testing data are identical. Thus, also test the out- of-distribution (OOD) robustness. Specifically, we use one dataset as the training (source) dataset and the other datasets as the testing (target) datasets.
Source Dataset
Fine- Tuninn Our Method
Biased Finance
54.89
80.36
Biased Medicine
56.31
80.48
Biased GovReport
54.34
85.74
Table 3: The average accuracy (%) on all target datasets of the fine-tuning method and our method in the OOD setting when using biased datasets as source datasets.
As shown in Table 2, the OOD performance of our method is better than the fine-tuning method on Medicine, Reddit Eli5, MultiNews and BillSum datasets. The fine-tuning method may overfit the training datasets and thus has poor OOD perfor- mance. Overall, our method more robust under the OOD setting. The reason is that the difference between degrees of revisions for the human-written texts and LLM-ggnerated texts is the universal fea- ture among various datasets.
tion performance of the methods that rely on the output losses of the proxy model will degrade. For the statistics of BARTScores and Log- Log-Likelihood scores, please refer to appendix A.
4.2.2 Comparisons with Fine-Tuiee Tine-Tunig
Comparisons in Vanilla Setting. We compare the accuracy of our method with the fine-tuning method. To calculate the accuracy of our method, we need determine threshold. If the test sam- ple's similarity score between and its revised text is no smaller than the threshold, is predicted as the LLM-gennrated text. The detailed steps to find the optimal threshold on the source dataset are as follows!. Firstly, we get the similarity scores for all samples in the source dataset and their ChatGPT- revised versions. We take the minimum similarity score and the maximum similarity score. Then, we split the range between the minimum similar-
Comparisons on Biased Datasets. The fine- tuning method is proven to easily overfit the dataset biases (McCoy al., 2019). The dataset biases are some spurious correlations that are not shared among all datasets of task (Lynch al., 2023). We construct the biased datasets by adding the pre- fix "Answer: " to the muman--ritter answers Fi- nance and Medicine datasets and adding the prefix "Summarization: " to the human-writte summaries of the GovReport dataset.
As shown in Table 3. for the fine-tuning method, the classification models that are fine-tuned on bi- ased datasets have very poor OOD performance, with all average accuracy falling below 60%. The reason is that the trained classification model with
'Frr the fine-tuning method, the source dataset is the train- ing datasct, which is used to train a classifier. Our method does not need to train classifier. For our method, we find the optimal threshold on the source datasct.

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BERTSecoe Finance Medicine
BARTScore
human-writted summary, which may change the sentences' structures. For example, a long human- written sentence with complex logic is revised into a relatively shorter sentence by ChatGPT. For more details, please refer to appendix A.
Source Model
Finance
Medicine
Tex--davinci-003
90.18
92.94
83.38
91.41
Text-davinci-002
86.83
86.21
88.90
90.81
Vicuna 78.68
88.40
95.43
97.88
Table 4: Performance of our method when detecting texts that generated by various source models. The evaluation metric is AUROC (%)
Conclusion
In this paper, we design a simple and effective baseline method for detecting LLM-gennrated texts. Our proposed method is based on the intuition that the ChatGPT model revises less for LLM-generated texts than it does for human-written texts. Com- pared with previous methods, our method neither needs to train DNN-based classifier requires access to the source model's oulput logits or losses. Thus, our method has better generalization ability and is more practical. We perform experiments on six datasets of question answering and summariza- tion generation tasks. The experimental results on various datasets show that our method can effec- tively detect LLM-generated texts. Moreover, our method is robust to dataset biases.
the fine-tuning method overfits dataset biases, i.c., the mapping from the prefix to the label "humann- written".. However, these dataset biases are not universal features among all datasets. Also, from the results in Table 3, we can see that our method is robust to the dataset bias. The average accuracy on all target datasets of our method above 80% with whichever biased dataset as the source dataset. The reason is that our method leverages the similarity between a pair of texts and is less influenced by the inserted prefix biases. For more details, please refer to appendix A.
4.3
Detecting Texts Generated by Various Source Models
Limitations
We demonstrate that our method can be applied to detect texts generated by various source mod- els such as text-davinci-003, text-davinci-002 and Vicuna (Chiang al., 2023). Firstly, we use the source model generate answers of the Finance and Medicine datasets. Then we leverage ChatGPT to revise texts. As shown in Table 4, our method can detect texts generated by various source mod- els. The AUROC is higher than 78% under all the evaluated settings. The revisions for other source models' generated texts minor compared with the revisions for human-written texts when using ChatGPT revise the texts. The texts generated by ChatGPT and other source models may share some similar characteristics. The deeper reason may be that the training data of ChatGPT and other source models may have similar or overlapping parts.
Although our method is simple and effective, it still has some limitations. Firstly, our method needs to query the ChatGPT model and thus may cost money. Secondly, if the text be detected is ex- tremely short, the difference of revisions for human- written and L.LM -..--aereetted texts may not be evi- dent, which may lower the detection performance of our method.
A Acknowledgments
This work is supported by the National Key R&D Program China (No.2022Z00116312), Na- tional Natural Science Foundation of China (No. 62236004) and Institute Guo Qiang at Tsinghua University.
Biru Zhu, Lifan Yuan and Chong Fu designed the methods. Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen and Bingxiang He designed the exper- iments. Yangdong Deng, Zhiyuan Liu, Maosong Sun and Ming Gu advised the project and partici- pated in the discussion.
4.4
Case Study
We conduct the case study on numan-writteed summary and the summary generated by Chat- GPT for the same bill from the BillSum dataset. From Figure 3 in the appendix, we can see that for the ChatGPT-generated summary, the revisions are mostly about synonym replacements or adding a word. However, from Figure 2 in the appendix, we can see that the revision is relatively large for the
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Appendix
of CharGPT-generated texts and their revised texts is lower than that of human-written texts for each dataset. This means that the extents of revisions vary largely among human-written texts while vary- ing little among ChatGPT-gennratee texts.
A Additional Experimental Results
Results of Different Similarity Metries. We evaluate the effectiveness of our method with dif- ferent similarity metrics, including BLEU score, ROUGE score, BERT and BARTScore-CNN, We use the similarity score as the prediction score and the labels of LLM-generated texts are set as 1. The experimental results are shown in Table 5. From the experimental results, we can see that the AUROC is above 60% no matter which similar- ity metric is used on all datasets, which demon- strates the effectiveness of our method. The results verify our intuition, i.c., compared with human- written texts and their corresponding revised texts, LLM-generated texts and their corresponding re- vised texts are more similar.
Lig-Likelihood Scores on Different Datasets. The Log-Likelihood method takes the negative loss of the model on the text to be detected as the Log- Likelihood score. A higher Log-Likelihood score means this piece of text is more likely to be LLM- generated. Since the output losses of ChatGPT not available at the inference time, use the GPT-2-medium model as the proxy model for the Log-Liieeihood method. We record the mean value of negative losses of the GPT-2-medium model (i.e., the Log- Ligeliield scores) on human-written texts and that on ChatGPTTggeeraate texts. As shown in Table 7, the losses derived from the GPT-2-medium model are high on the ChatGPT -egnerrttd texts of MultiNews, GovReport and BillSum.
Besides, we can find that the performance of our detection method varies with different sim- ilarity metrics. For example, when the similar- ity metric is BARTScore-CNN, the average AU- ROC on all datasets is 90.05%. When the simi- larity metric is BLEU score, the average AUROC on all datasets is 77.29%. The BLEU score and ROUGE score are based n-gram matches. The BERTScore and BARTScore-CNN CNN are DNN-based similarity metrics. To sum up, the ONN-based similarity metries perform better than those based on n-gram matches. The reason may be that the DNN-based similarity metrics can capture the se- mantic similarity better than those based on n-gram matches. The BERTScore performs better than BARTScore-CNN on MultiNews, GovReport and BillSum datasets. However, the BARTSCore-CNNN achieves the highest average performance among all similarity metrics.
Results of Comparisons with Fine- Tine-Tuning in Vanilla Setting, We compare the accuracy (ACC) of our method with the fine-tuning method. Besides considering the setting that the training (source) and testing (target) data have the same distribu- tion, we also test OOD robustness. For the ex- periments where the training (source) and testing (target) datasets are of the same distribution, we evaluate the detection performance in the classic cross-validation manner. For the experiments of testing OOD robustness, we use one dataset as the training dataset (source dataset) and a different dataset as the testing dataset (target dataset).
The experimental results are shown in Table 8. We can see that the fine-tuning method achieves good performance when the training and testing data have the same distribution. However, as shown in Table 8, there are 14 results whose accuracy are below 60% for the fine-tuning method. Also, we can see that there are 2 results whose accuracy are below 60% for our method. From this point of view, our method has better generalization ability compared with the fine-tuning method. The trained classification model easy to overfit the training data for the fine-tuning method. Thus, the gener- alization ability of the fine-tuning method is poor. Our method is more robust under the OOD setting.
Statistics of BARTScores. For both human- written texts and ChatGPT-generated texts, we record the mean value and variance BARTScores between the original texts and their corresponding revised texts. We use BARTScore-Chh -NNN as the sim- ilarity metric to calculate the BARTScores. The higher BARTScore indicates the higher similarity between the text and its corresponding revised text. As shown in Table 6, the average BARTScore of ChatGPT-gennratee texts and their revised texts is higher than that of human-written texts for each dataset. The ChatGPT model revises more for humann human-written written texts compared with ChatGPT- generated texts. Also, the variance of BARTScores
Results of Comparisons with Fine-Tuning on Biased Datasets. The dataset biases are some spurious correlations that are not shared among

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Similarity Metric
Finance
Medicine
Reddit EliS
MultiNews
GovReport
BillSum
Average Value
BLEU Score
83.93
90.18
83.80
70.73
69.55
65.54
77.29
ROUGE-1 Fl Score
85.43
90.28
82.34
73.16
68.69
70.25
78.36
ROUGE-2 Fl Score
87.60
90.18
81.17
69.85
65.18
63.40
76.23
ROUGE-L FI Score
87.23
87.48
80.43
72.10
64.72
66.13
76.35
BERT'score
84.41
89.47
89.87
86.73
83.55
91.62
87.61
BARTScore-CNN 97.40
95.06
99.15
86.28
75.64
86.77
90.05
Table Performance of our method using different similarity metrics. The evaluation metric is AUROC (%). The Average Value" is the average performance all datasets.
Dataset H-Mean
H-Var C-Mean
Car
are biased datasets. As shown in Table 9, for the fine-tuning method, the OOD performance of the classification models that are fine-tuned on biased datasets is very poor, with the accuracy below 60% in most evaluated situations. The reason is that the trained classification model with supervised line-tuming overfits dataset biases, i.e., the mapping from the prefix "Answer: or "Summarization: to the label "human-written". However, these dataset biases are not the universal useful features among all datasets and they can not be used for classifying the human- wwitten and LLM-generated texts of other datasets without these biases. Thus. the OOD performance of the fine-tuning method declines much when the training dataset contains biases. Besides, shown in Table 9, our method robust the dataset bias. Under all evaluated situations, the accuracy of our method above 65%, which demonstrates that our method is still effective when the source dataset contains biases.
Finance
-2.504
0.271
-1.373
0,088
Medicine
-2.780
0.493
-1.60
0.339
Reddit EliS
-3.373
0.331
-1.651
0.161
MultiNews
-2,250
0.209
-1.614
0.127
GovReport -1.826
0.218
-1.411
0.125
BillSum
-2.004
0.200
-1.401
0.099
Table 6: Statistics of BARTScores computed on human- ereeeeaCChaGPPP-geeenead texts and their correspond- ing revised texts. The prefix "H" and "C" repre- sent "human-written" and "ChaiGPT-generatee" spectively. "Mean" and "Var" represent mean value and variance, respectively.
re-
Dataset
H-Score
C-Score
Finance
-3.355
-1.864
Medicine
-3.888
-1.893
Reddit Eli5
-3.89
-2.022
MultiNews
-2.944
-2.941
GovReport -2.634
-2.788
BillSum -2.526
-2.694
Table The mean value of negative losses (Log- Likelihood scores) on human-written texts and that on texts. "H-Score"/"C-Se "ccooo""" rep- resents the mean value of negative losses human- written texts/ChatGPT-ggneee... texts.
Results of the Case Study. We show examples of human-written summary and the summary generated by ChatGPT for the same bill from the BillSum dataset (Kornilova and Fidelman, 2019). Specifically, we show the human-writter summary and its CharGPT-revised version in Figure 2. The CharGPT--eenertte summary and its ChatGPT- revised version are shown in Figure 3. Some re- vised parts in the revised texts are highlighted in red color. From Figure and Figure 3, that the revisions are more obvious for the human- written summary. For example, the human-writter sentence "Prescribes procedural guidelines for Nonperformance of Transportation" revised into the sentence "It also establishes guidelines for Nonperformance of Transportation offers the ser- vice". The ChatGPT model revises the long human- written sentence into relatively shorter sentence.
all datasets of a specific task (McCoy et al., 2019;
Lynch et al., 2023). We select datasets as the source datasets that the classification model is fine- tumed on, including Finance, Medicine, and Gov- Report. The classification models trained on these three original datasets have relatively good OOD performance, as shown in Table 8. We construct the biased datasets by adding the prefix "Answer: to the human-written answers of Finance and Medicine datasets and adding the prefix "Summa- rization: " to the human-written summaries of the GovReport dataset. We do not perform any addi- tional operations the LLM-generated texts.
We show the similarity scores between the human-winn written summary and its corresponding re- vised text, and the similarity scores between the
We evaluate the fine-tuning method and our method the scenario where the source datasets

---

Nource/Target Dataset
Finance
Medicine
Reddit Elis
MultiNews
GovReport
BillSum
Fine-Tuning
Finance
99.66
87.32
92.61
70.75
82.95
Medicine
88.92
95.48
44.11
53.07
55.16
Reddit EliS
$2.15
99.99
49.98
50.02
50.98
MultiNews
51.50
48.89
$1.15
100.0
51.12
53.38
GovReport
75.58
85.81
72.77
90.19
96.38
93.74
BillSum
72.63
66.37
78.21
50.13
57.83
99.97
Our Method
Finance
93.72
88.92
87.60
77.35
68.26
78.14
Medicine
91.97
90.25
93,41
77.85
65.44
74.41
Reddit EliS
79.95
84.39
97.51
68.85
56.71
59.74
MultiNews
92.72
90.65
92.01
77.9
66.35
75.46
GovReport
92.91
84.95
81.51
75.18
69.22
78.79
BillSum
93.10
86.32
83.18
75.65
68.69
78.59
Table 8: Results of the fine-tuning method and our method when using original datasets as source datasets. The evaluation metric is ACC (%)
Taurce/Target Dataset
Finance
Medicine
Reddit Eli5
MultiNews
GovReport
BillSum
Fine-Tuning
Biased Finance
100.0
53,44
58.55
$3.43
52.57
56.46
Biased Medicine
59.07
100.0
72.60
49.78
50.05
50.05
Biased Gov Repoet
53.44
54.96
53.17
57.40
100.0
52.73
Our Method
Biased Finance 94.93
89.65
88.57
77.85
67.94
77.79
Biased Medicine
91.76
92.30
93.64
77.8
65.02
74.16
Biased GovReport
93.01
90.37
90.78
78.03
88.76
76.49
Table 9: Results of the fine-tuning method and method when using biased datasets evaluation metric is ACC (%). For the experiments where the training (source) and testing (target) datasets are of the same distribution, the training (source) and testing (target) data are both with dataset biases. For the experiments of testing OOD robustness, the source dataset has the dataset bias while the target dataset (different from the source dataset) does not have the dataset bias.
datasets. The
Similarity Metric Human- Written ChatGPT-Generated
B Details of Similarity Metrics
BLEU Score
0.272
0.794
The details of the similarity metrics are as follows.
ROUGE-I FI Score
0.589
0.904
ROUGE-2 FI Score
0.377
0.823
BLEU score (Papineni et al., 2002): The BLEU score is calculated based on over- lap between the hypothesis's 's n-grams and the reference's 's n-grams. In our scenario, the ref- erence is the text to be detected and the hy- pothesis is its corresponding revised text. The higher BLEU score indicates the higher simi- larity between the text and its revised text.
ROUGE-L Score
0.529
0.865
BERTScore 0.538
0.909
BARTScore-CNY -2.67
-0.711
Table 10: Similarity scores between the human- written/ChatGPT--een -egeeratd summary and its revised text case study, measured by different similarity metrics.
ROUGE score (Lin, 2004): Similar to the BLEU score, the ROUGE score is also based on the overlap between the hypothesis's n- grams and the reference's n-grams, The higher ROUGE score indicates the higher sim- ilarity between two pieces of texts.
ChatGPT-gennrated summary and its correspond- ing revised text under different similarity metrics in Table 10. From the results in Table 10, can that the similarity score between the ChatGPT- generated summary and its corresponding revised text is higher than that between the human-written summary and its corresponding revised text no mat- ter which similarity metric is used.
BERTScore (Zhang* et al., 2020): The BERTScore is calculated based on the co-

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Human- -rrittnn Text
Authorizes passenger transportation in foreign-flag cruise vessels between Alaska ports, and between Alaska ports and those the west coast of the contiguous States.
Revised Human-Written Text
This legislation permits travel for passengers foreign- flaignn-lag cruise vessels between Alaskan ports and between Alaskan ports and those on the west coast of the contiguous United States. Ir also establishes guidelines for the Secretary
Preseribes procedural guidelines under which the Secretary Transportation shall notify owner or operator of one or more foreign-flag vessels that he shall terminate the authorization for the foreign-fle wfaggn-laag vessel to provide passenger transportation upon showing by the owner or charterer of a U.S. cruise vessel that the U.S. vessel is offering such passenger service pursuant to Certificate Financial Responsibility for Indemnification of Passengers for Nonperformance of Transportation.
Transportation to inform the owner or operator of foreign-flag flag vessel that it must cease passenger transportation iff vessel with a Certificate of Financial Responsibility for Indemnification of Passengers for Nonperformance of Transportation offers the service. These privileges for the foreign- flag vessel will expire 365 days after receiving the Secretary's notification of termination.
States that coastwise privileges granted a foreign-flag cruisc vessel under this Act shall expire the 365th day following receipt of Secretary's termination notification.
Figure 2: The human-written text and its ChatGPT-revised version.
ChatGPT-Generatte Text
Revised ChatGPT-GGnnratee Text
The text discusses congressional finding regarding the growing cruise ship industry in Alaska and the need to encourage the use of US facilities, labor, and services. It notes the importance of protecting existing US employment and economic activity and highlights the significant economic benefits received by Vancouver through the industry. The text also permits the transportation of passengers in foreign- flag cruise vessels between ports in Alaska and on the west coast of the contiguous states, provided certain conditions are met. It allows coastwise privileges US cruise vessels and outlines process for terminating foreign-flag vessels' service after notification period. The text provides detinitions and a disclaimer regarding the tramsportation of passengers and merchandise in Canadian vessels between ports in Alaska and the US.
The text discusses a congressional finding regarding the growing cruise ship industry in Alaska and emphasizes the need to prioritize US facilities, labor, and services. stresses the importance of protecting existing US employment and cconomic activity while recognizing the significant cconomic benefits received
by Vancouver through the industry. The text also permits the transportation of passengers in foreign-flag cruise vessels hetween ports in Alaska and on the west coast of the contiguous states, (uuject to specific conditions. It grants coastwise privileges for US cruise vessels and outlines a process for terminating foreign- flag vessels' service after a notice period. Additionally. the text provides definitions and disclaimer regarding the transportation of passengers and merchandise Canadian vessels between ports in Alaska and the US.
Figure 3: The ChaiGPT-generated text and its ChatGPT-revised version.
sine similarities of pairwise tokens' represen- tations of two sentences. The tokens' rep- resentations are encoded by the pre-trained language model. The higher BERTScore indi- cates the higher similarity between two pieces of texts.
of the BART model and the target text. The higher BARTScore indicates the higher simi- larity between the text to be detected and its corresponding revised text.
C Details of the Compared Detection Methods
BARTScore (Yuan et al., 2021): :): For the calcu- lation of BARTScore in this paper, we use the BARTScore-CNN, which uses BART model that is fine-tuned on the CNNDM dataset (Her- mann al., 2015). The revised text is input to the BART model and the original text to be detected is the target text. The BARTScore is the negative loss between the output logits
The details of the detection methods we compare our method with are as follows.
(1) Log-Likelihood (Solaiman al., 2019): The text that needs be detected is fed into the GPT-2- medium model. The Log-Likellihood score is the negative output loss of the model. A higher Log- Likelihood score means the text is more likely to

---

Statistics
Finance
Medicine
Reddit Elis
MultiNews
GovReport
BillSum
Number of Samples
7866
2492
29190
4000
1878
3994
Average Length of H--Text
175.6
82.2
147.9
202.1
303.1
182.1
Average Length of Revised H-Texts
136.3
97.6
102.0
158.8
223.8
156.3
Average Length C-Texxs
205.2
187.4
173.6
102.3
151.9
129.1
Average Length of Revised C-Texts
168.7
161.1
140.8
103.8
147.6
126.8
Table I::
Dataset statistics. "H-Texts" and "C-Texts" represent the human-written texts and ChaiGPT-generated texts, respectively. The number of samples for each dataset takes both human-written samples and ChatGPT- generated samples into account.
be LLM--genrratd.
D Implementation Details
D.1 Experimental Setting
(2) Rank (Gehrmann et al., 2019): The text that needs be detected is fed into the GPT-2-medium model. Then this method sorts the output logits in descending order for each token. After sorting, the method gets the rank of each label token. Then the method gets the final rank score by averaging the rank scores of all label tokens. A lower rank score means the text is more likely to be generated by the LLM.
Datasets. For the summarization generation task, we perform experiments on the MultiNews (Fab- bri et al., 2019), GovReport (Huang et al., 2021) and BillSum (Kornilova and Eidelman, 2019) datasets. For Human--ritten texts, we directly use the human- datasets. In the main experiments, use the rep- resentative LLM, i... ChatGPT (gpt-3.5-turbo), the source model to generate the LLM LLM-generated texts to be detected. We use the prompt "Please summarize the following text: " to make the Chat- GPT model generate the summaries and get the ChaiGPT-generated summaries. For the question answering task, the datasets we consider are Fi- nance (Maia et al., 2018), Medicine (Chen et al., 2020), and Reddit Eli5 (Fan al., 2019). In the main experiments, we use the ChatGPT-gennertted texts collected by Guo al. (2023) for these three datasets. Specifically, we use the first human- written answer in the Humman-written answer list and the first ChatGPT--ennerate answer in the ChatGPT-gennratee answer list for each question. For some datasets, we sample some samples from the original datasets. The detailed statistics of the number of samples and the average length of sam- ples for each dataset are shown in Table 11.
summaries in the original
(3) Log-Raun Rank (Mitchell et al., 2023): A little different from the Rank method, the Log-Rank method just adds an additional operation by apply- ing a log function on the rank score of each token. Similar to the rank score, lower log-rank score means the text is more likely to be --gegenrraeed.
(4) Entropy (Gehrmann et al., 2019): The text that needs to be detected is fed into the GPT-2- medium model. The Entropy method calculates the entropy of the softmax logits derived from the GPT- 2-medium model and then averages the entropy of each token to get the final entropy score. A lower entropy score means the text is more likely to be ..L.-eenrr
(5) DetectGPT (Mitchell al., 2023): For the DetectGPT method, perturbs the text to be de- tected using the T5-large (Raffel et al., 2020) model and gets the perturbed text. Then it applies a log function on the ratio of the original text's proba- bility to the perturbed text's probability to get the final ratio. If this ratio is high, it means the text is likely be LLM-generated.
When we revise texts with ChatGPT, we use the gpt-3.5-turbo API provided by OpenAL. When gen- crating ChatGPT-generated summaries for Multi- News, GovReport and BillSum datasets, we use the gpt-3.5-turfe API provided by OpenAI.
All experiments that call gpt-3.5-turb API in this paper are done before June 2023, with the gpt- 3.5-turbo API being gpt-3.5-turo-0301..
(6) Supervised Fine-Tuning (Guo et al., 2023):
For the implementation of the supervised fine- tuning method, we fine-tune the RoBERTaBASE (Liu et al., 2019) model on the labeled training samples to get a classification model.
Similarity Metries. For the calculation of BART all experiments in this paper, we use the BARTScore-CNE which uses a BART model that is fine-tuned on the CNNDM dataset (Her-

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mann et al., 2015). For the ROUGE score and BERTScore, we use the Fl score.
model and test the performance for 4 turns. In each turn, we use 3 parts as the training part and the remaining part as the testing part. In each turn, we use a part that is different from the test- ing parts in other turns as the testing part for this turn. Finally, we use the total number of correctly predicted testing samples in 4 turns to divide the total number of testing samples in 4 turns and get the final accuracy.
D.2 Main Experiments
Comparisons in Zero-Shot Setting. When cal- culating the AUROC performance of other zero- shot detection methods, we use the Log- Log-Likelihood score, negative rank score, negative log-rank score, negative entropy score and log probability ra- tio as the prediction score, respectively, for Log- Likelihood, Rank, Log-Rani: Entropy and Detect- GPT methods. The labels of ChatGPT-generated texts are set as
The source dataset on which the classification model is fine-tuned is the biased dataset. For the experiments where the training (source) and testing (target) datasets are of the same distribution, the training (source) and testing (target) data are both with dataset biases. For the experiments of testing OOD robustness, the source dataset has the dataset bias while the target dataset (different from the source dataset) does not have the dataset bias.
For the Medicine dataset, we drop pair of human-written and ChatGPT-generatee answers of question for other zero-shot detection methods, due to the NaN value of the Log-Likelihodd score. For the implementation of DetectGPT, we use the T5-large as the mask filling model and the number of perturbations is set as 10. The perturbation mode is set as "z" for DetectGPT.
For the experiments of testing the OOD robust- ness, we fine-tune the classification model with three different seeds and get the average OOD per- formance as the final OOD performance for the fine-tuning method.
Comparisons with Fine-Tuning in Vanilla Set- ting. For the experiments where the training and testing datasets are of the same distribution, evaluate the detection performance in the classic cross- pross-validation manner. Specifically, we first split the whole dataset into 4 parts. We train the model and test the performance for 4 turns. In each turn, we use 3 parts as the training part and the remaining part as the testing part. In each turn, we use a part that is different from the testing parts in other turns the testing part for this turn. Finally, the total number of correctly predicted testing sam- ples in 4 turns to divide the total number of testing samples in 4 turns and get the final accuracy.
For our method, we use the BARTScore-CNN as the similarity metric.
D.3 Detecting Texts Generated by Various Source Models
For generating LLM L.M--gnnrated texts with Vicuna, we first obtain a Vicuna model using the LLaMA- 7B model and the delta weight", following the official implementation'. Then we use the Vicuna model to generate texts.
use
Some other details of the experiments are fol- lows. For generating LLM--generted texts with the text-davinci-002- and Vicuna models, we use the instruction "Please generate a long answer for the following question: " For generating LLM- generated texts with the coxt-davinci-000 model we do not add any additional instruction and just input the questions of the Finance and Medicine datasets into the model. For experiments of detect- ing the text-davinci-002 model's generated texts for the Medicine dataset, we drop 15 pairs of human- written and LLM-generated texts that correspond
For the experiments of testing the OOD robust- ness, we fine-tune the classification model with three different seeds and get the average OOD per- formance the final OOD performance for the fine-tuning method. The number of training epochs is set as 10 and the learning rate is set as 2 10 for the fine-tuning method.
For our method, we use the BARTScore-CNN as the similarity metric to calculate similarity scores.
Comparisons with Fine-Tuning Datasets. For the experiments where the train- ing and testing datasets are of the same distribution, we evaluate the detection performance the clas- sic cross validatii manner. Specifically, we first split the whole dataset into 4 parts. We train the
Biased
http:://mmnggingffeee...eeaaaeee ma-76-1 7b-hf htttes///hngiinoeeee..////ssssss
-v1.1 htttpss//gituub..m///mmssss/se/e
2 Phttps://openai.coo hnttps://openii.com

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15 questions due the blank answers returned by the text-davinci-002 model. We evaluate the detection performance of our method using the BERTScore and BARTScore-CNN as the similar- ity metric, respectively.


Reviews:
[{"review_id": "hCqUwwa4H1", "reviewer": "Reviewer_EYmp", "summary": "", "questions": "Is there a reason the XSum, SQuAD, WritingPrompts datasets which are originally used to evaluate DetectGPT (your competitive baseline) are excluded in the evaluation? Including those datasets would make your method more comparable with DetectGPT. Given that DetectGPT performs worse on average than the Log-Rank baseline in your benchmarks, but outperforms it in the DetectGPT benchmarks/paper, may indicate that there is discrepancy in the set of tasks used for evaluation.\n\nWould applying your heuristic work when the generator model and the revision model are farther away from each other? In most experiments both generated texts and revision model are GPT-3.5 turbo, with the exception of Vicuna in table 4 (which was trained using GPT-4 as well). Would the heuristic work if your texts were generated using PALM or Claude models?\n\nMachine generated text detection is a hard problem when casted as binary classification. For instance, OpenAI recently stopped their AI generated detection classifier, citing poor performance as a reason. It seems that for any system to truly be able to detect machine generated text, much more accuracy in detection is desired. Would you consider this to be a limitation of your approach?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the valuable suggestions and insightful comments. Our point-to-point responses are shown as follows.\n\n***Comment 1:*** **\"Baseline comparisons may be faulty: For the \"zero shot methods for comparison\" the authors use GPT-2 logits to calculate Log Likelihood, Rank, Log Rank, and Entropy for input texts. However the input generated text is sampled from GPT-3.5 turbo, a completely different model, which greatly undermines the point of using log likelihood and similar proxy measures for detecting machine generated text. The competing baseline DetectGPT uses similar zero-shot baselines for comparison, but they calculate log likelihood and other metrics using the logits from the same model that produced the generated text, making log likelihood more comparable with their method.''**\n\n**Response:** \nWe thank the reviewer for the comment. It is well-known that ChatGPT-generated texts are of higher quality than GPT2-generated texts. As a result, people prefer to use more advanced LLMs. Thus, in our paper, we focus on detecting texts generated by advanced LLMs, e.g., ChatGPT. However, many advanced LLMs provided by commercial companies, e.g., ChatGPT, do not expose their output logits/losses at the inference time, which makes those logits/losses-based detection methods not applicable. So we use a proxy model to derive logits/losses for those detection methods (e.g., DetectGPT[1]), which rely on the access to output logits/losses of the model. Following MGTBench[2], we use the GPT-2-medium as the proxy model to derive the output logits/losses for Log-Likelihood, Rank, Log-Rank, Entropy and DetectGPT methods. \n\nReferences:\n\n[1] DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature.\n\n[2] MGTBench: Benchmarking Machine-Generated Text Detection.\n\n***Comment 2:*** **\"Is there a reason the XSum, SQuAD, WritingPrompts datasets which are originally used to evaluate DetectGPT (your competitive baseline) are excluded in the evaluation? Including those datasets would make your method more comparable with DetectGPT. Given that DetectGPT performs worse on average than the Log-Rank baseline in your benchmarks, but outperforms it in the DetectGPT benchmarks/paper, may indicate that there is discrepancy in the set of tasks used for evaluation.\"**\n\n**Response:** \nWe thank the reviewer for the insightful comment. Following the reviewer's suggestions, we perform new experiments to detect LLM-generated texts on a subset of the WritingPrompts dataset. Specifically, we use the GPT-3.5 turbo model to generate stories based on the prompts in the WritingPrompts dataset for deriving the LLM-generated texts. Then we use our method and other zero-shot detection methods to detect LLM-generated texts. The implementations of other zero-shot detection methods follow the paper MGTBench[1]. The experimental results are shown in Table 1. From the experimental results, we can see that the DetectGPT method performs worse than our method on the WritingPrompts dataset.\n\n|          &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Method         | WritingPrompts |\n| :------------------------: | :------------: |\n|       Log-Likelihood       |     99.08      |\n|            Rank            |     98.71      |\n|          Log-Rank          |     99.33      |\n|          Entropy           |     95.63      |\n|         DetectGPT          |     61.46      |\n| Our Method (BARTScore-CNN) |     99.12      |\n\n\nTable 1: Comparisons with other zero-shot detection methods on the WritingPrompts dataset. The evaluation metric is AUROC (%). We sample 2000 human-written texts and 2000 ChatGPT-generated texts for the WritingPrompts dataset. \n\nWe think the discrepancy between our results and the original results reported in the DetectGPT paper is due to the **black-box setting**. DetectGPT[2] performs experiments under the **white-box** setting, where the output loss/logits of the source model are available for detection. However, we perform experiments under the **black-box** setting, where the detector cannot access the output logits/losses of the source model. Existing zero-shot detection methods, which rely on the output logits/losses of the source model, cannot be directly applied under such a black-box setting. To compare with those methods, following MGTBench[1], we use a proxy model to derive the output logits/losses for Rank, Log-Rank, Log-Likelihood, Entropy and DetectGPT methods when detecting ChatGPT-generated texts.\n\nIn fact, it is normal that DetectGPT performs worse on average than the Log-Rank baseline in our benchmarks (black-box setting). The authors of DetectGPT have reported that their method is most suited to the white-box setting and the detection performance is reduced if they use a surrogate model different from the source model, which is shown in Figure 6 of the DetectGPT paper[2]. The experimental results in Table 2 of the paper MGTBench[1] have also shown that the DetectGPT method performs worse than the Log-Rank method when detecting ChatGPT-generated texts using the GPT-2-medium model as the proxy model.\n\nThe black-box setting is more **practical** because public APIs or web services provided by commercial companies generally **do not** expose the output loss/logits of the model during inference, e.g., the web services of ChatGPT and Claude2. Moreover, the detector may **not know** the exact source model that generates the texts. Considering those facts, the detector cannot directly use the output logits/losses of the source model for detection in some cases.\n\nReferences:\n\n[1] MGTBench: Benchmarking Machine-Generated Text Detection.\n\n[2] DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature.\n\n***Comment 3:*** **\"Would applying your heuristic work when the generator model and the revision model are farther away from each other? In most experiments both generated texts and revision model are GPT-3.5 turbo, with the exception of Vicuna in table 4 (which was trained using GPT-4 as well). Would the heuristic work if your texts were generated using PALM or Claude models?\"**\n\n**Response:**\nWe thank the reviewer for the valuable suggestions. Following the reviewer's suggestions, we perform new experiments to use our method to detect texts generated by Claude-2 and Chat-bison-001 (based on PaLM2) models. We still use the GPT-3.5 turbo model for revision. From the experimental results in Table 2, we can see that our method can effectively detect texts that are generated by Chat-bison-001 (based on PaLM2) and Claude-2 models. For example, our method achieves an AUROC of 98.61% when detecting texts generated by the Chat-bison-001 model on the Reddit Eli5 dataset.\n\n|     &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Source Model      | Medicine | Finance | Reddit Eli5 |\n| :-------------------: | :------: | :-----: | :---------: |\n| Chat-bison-001 (based on PaLM2) |  92.34   |  91.13  |    98.61    |\n|       Claude-2        |  85.14   |  75.13  |    95.18    |\n\nTable 2: Performance of our method when detecting texts that are generated by Claude-2 and Chat-bison-001 models. The evaluation metric is AUROC (%). The similarity metric we used for our method is BARTScore-CNN. The experiments are conducted on three datasets, including Medicine, Finance and Reddit Eli5. We sample 300 human-written texts and 300 LLM-generated texts for the Finance dataset. We sample 100 human-written texts and 100 LLM-generated texts for the Reddit Eli5 dataset.\n\n***Comment 4:*** **\"Machine generated text detection is a hard problem when casted as binary classification. For instance, OpenAI recently stopped their AI generated detection classifier, citing poor performance as a reason. It seems that for any system to truly be able to detect machine generated text, much more accuracy in detection is desired. Would you consider this to be a limitation of your approach?\"**\n\n**Response:**\nWe thank the reviewer for the insightful comment. We admit that machine-generated text detection is a hard problem when cast as a binary classification problem. However, formulating the problem as a binary classification task offers a straightforward way to explore the possibility of detecting LLM-generated texts. In fact, previous works [1,2] also adopt such a formulation. Moreover, our paper provides a new idea for solving the machine-generated text detection problem, i.e., the ChatGPT model makes fewer revisions to LLM-generated texts than it does to human-written texts. Our idea still holds even if the problem is not cast as a binary classification problem. Also, we use the similarity score as the likelihood that a piece of text is generated by the LLM. A higher similarity score indicates that the piece of text is more likely to be generated by the LLM. Our method is based on the statistical probability of whether a piece of text is generated by the LLM rather than just training a supervised detector.\n\nReferences:\n\n[1] Automatic detection of machine generated text: A critical survey. \n\n[2] DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature.", "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": "The proposed method brings about improvements in detecting machine generated text, which is an important task for preventing misuse of large language models. The authors provide promising findings from evaluation on several baselines across multiple datasets.", "reasons_to_reject": "Baseline comparisons may be faulty: For the \"zero shot methods for comparison\" the authors use GPT-2 logits to calculate Log Likelihood, Rank, Log Rank, and Entropy for input texts. However the input generated text is sampled from GPT-3.5 turbo, a completely different model, which greatly undermines the point of using log likelihood and similar proxy measures for detecting machine generated text. The competing baseline DetectGPT uses similar zero-shot baselines for comparison, but they calculate log likelihood and other metrics using the logits from the same model that produced the generated text, making log likelihood more comparable with their method.\n\n\n", "questions_to_authors": "Is there a reason the XSum, SQuAD, WritingPrompts datasets which are originally used to evaluate DetectGPT (your competitive baseline) are excluded in the evaluation? Including those datasets would make your method more comparable with DetectGPT. Given that DetectGPT performs worse on average than the Log-Rank baseline in your benchmarks, but outperforms it in the DetectGPT benchmarks/paper, may indicate that there is discrepancy in the set of tasks used for evaluation.\n\nWould applying your heuristic work when the generator model and the revision model are farther away from each other? In most experiments both generated texts and revision model are GPT-3.5 turbo, with the exception of Vicuna in table 4 (which was trained using GPT-4 as well). Would the heuristic work if your texts were generated using PALM or Claude models?\n\nMachine generated text detection is a hard problem when casted as binary classification. For instance, OpenAI recently stopped their AI generated detection classifier, citing poor performance as a reason. It seems that for any system to truly be able to detect machine generated text, much more accuracy in detection is desired. Would you consider this to be a limitation of your approach?", "paper_topic_and_main_contributions": "The paper proposes a new method for detecting machine (LLM) generated text. The authors build on the core idea that machine generated text is editted fewer times in machine generated revisions of the same text. So in order to detect whether an input is machine generated, the authors query ChatGPT (GPT-3.5 Turbo) to revise the input, and use the similarity between the revised text and the original to determine whether the text is machine generated. The proposed approach beats DetectGPT and several other baselines that are mainly built on GPT-2.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "avpPmA8GIw", "reviewer": "Reviewer_zd6D", "summary": "", "questions": "If human text is included in the LLM training (fine-tuning) data, can your method successfully identify it?", "limitations": "", "rating": 3, "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": "We thank the reviewer for the valuable suggestions and insightful comments. Our point-to-point responses are shown as follows.\n\n***Comment 1:*** **\"There is no real different source model on Table 4. Vicuna is finetune on the ShareGPT data. Maybe a experiment on claude?\"**\n\n**Response:** \nWe thank the reviewer for the valuable suggestions. Following the reviewer's suggestions, we perform new experiments to use our method to detect texts generated by Claude-2 and Chat-bison-001 (based on PaLM2) models. We still use the GPT-3.5 turbo model for revision. From the experimental results in Table 1, we can see that our method can effectively detect texts that are generated by Chat-bison-001 and Claude-2 models. For example, our method achieves an AUROC of 98.61% when detecting texts generated by the Chat-bison-001 model on the Reddit Eli5 dataset.\n\n|     &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Source Model      | Medicine | Finance | Reddit Eli5 |\n| :-------------------: | :------: | :-----: | :---------: |\n| Chat-bison-001 (based on PaLM2) |  92.34   |  91.13  |    98.61    |\n|       Claude-2        |  85.14   |  75.13  |    95.18    |\n\nTable 1: Performance of our method when detecting texts that are generated by Claude-2 and Chat-bison-001 models. The evaluation metric is AUROC (%). The similarity metric we used for our method is BARTScore-CNN. The experiments are conducted on three datasets, including Medicine, Finance and Reddit Eli5. We sample 300 human-written texts and 300 LLM-generated texts for the Finance dataset. We sample 100 human-written texts and 100 LLM-generated texts for the Reddit Eli5 dataset.\n\n***Comment 2:*** **\"If human text is included in the LLM training (fine-tuning) data, can your method successfully identify it?\"**\n\n**Response:** \nWe thank the reviewer for the insightful comment. The datasets we used for evaluation are all publicly available, e.g., the Reddit Eli5 dataset. We do not know the exact training data the ChatGPT model used for fine-tuning. Some blogs and technical reports have mentioned that some Reddit data may have been used for training ChatGPT. As the ChatGPT model has been trained on massive publicly available data, some publicly available data we used for evaluating our method may be already included in the training data of the ChatGPT model.\n\n***Comment 3:*** **\"Typos Grammar Style And Presentation Improvements: Perhaps it would be better to highlight some data in the table by bolding certain values, such as the maximum value of each column.\"**\n\n**Response:** \nWe thank the reviewer for the valuable suggestions. We will improve the grammar style and the presentation of our paper in the camera-ready version, including fixing typos and highlighting some important data in the table.", "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": "The idea is novel, simple and efftive. It shows good generalization.\n", "reasons_to_reject": "There is no real different source model on Table 4. Vicuna is finetune on the ShareGPT data. Maybe a experiment on claude?", "questions_to_authors": "If human text is included in the LLM training (fine-tuning) data, can your method successfully identify it?", "paper_topic_and_main_contributions": "This paper investigates the detection of LLM generated texts.\nThe auhtors propose to measure the similarity between the given text and LLM revised version, the given text would likely be LLM generated if they are similar.\nThey conduct extensive experiments and achieve the best average AUROC among all zero-shot methods.\nWhen comparing with fine-tuning based models, the suggest approach shows strong OOD genelization ability.\nThe detection of different source LLMs (text-davinci-003/2 and vicuna) are also tested.\n", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "Perhaps it would be better to highlight some data in the table by bolding certain values, such as the maximum value of each column."}, "extra_text": {}}, {"review_id": "4ENt1FCpaT", "reviewer": "Reviewer_s23n", "summary": "", "questions": "", "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": "We thank the reviewer for the valuable suggestions and insightful comments. Our point-to-point responses are shown as follows.\n\n***Comment 1:*** **\"The continuous reference to appendices bothers the reading: some crucial information is reported in the appendix (such as the study on the metric threshold in app E).\"**\n\n**Response:**\nWe thank the reviewer for the valuable suggestions. Following the reviewer's suggestions, we will move some crucial information that is reported in the appendix to the main body of the paper in the camera-ready version.\n\n***Comment 2:*** **\"The revision step of the approach employes only chatgpt, thus we do not know if the method shows similar performances also when a different model is employed for revision.\"**\n\n**Response:**\nWe thank the reviewer for the valuable suggestions. Following the reviewer's suggestions, we employ some other models for revision, including the GPT-4 model and the Claude-2 model. The experimental results are shown in Table 1. From the experimental results, we can see that our method can achieve similar performance when using a different model for revision. For example, when using the GPT-4 model for revision, our method achieves an AUROC of 99.91% on the Reddit Eli5 dataset.\n| The Model for Revision | Medicine | Finance | Reddit Eli5 |\n| :-------------: | :------: | :-----: | :---------: |\n|      GPT-4      |  95.88   |  97.58  |    99.91    |\n| ChatGPT (turbo) |  96.72   |  98.73  |    99.95    |\n|    Claude-2     |  95.98   |  94.65  |    96.31    |\n\nTable 1: Performance of our method when using GPT-4, ChatGPT (turbo), and Claude-2 models for revision. The evaluation metric is AUROC (%). The similarity metric we used for our method is BARTScore-CNN. The experiments are conducted on three datasets, including Medicine, Finance and Reddit Eli5. We sample 100 human-written texts and 100 ChatGPT-generated texts for each dataset.", "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": "The paper presents a simple, yet effective, approach for addressing a timely task: identifying automatically generate texts and distinguishing them from human generate texts. \nThe approach is tested on texts acquired from six datasets pertaining to question&answering and summarization.\nThe authors perform experiments with texts generated by multiple source models, although they test revision only using chatgpt.", "reasons_to_reject": "The continuous reference to appendices bothers the reading: some crucial information is reported in the appendix (such as the study on the metric threshold in app E).\nThe revision step of the approach employes only chatgpt, thus we do not know if the method shows similar performances also when a different model is employed for revision.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper presents a \"zero-shot black-box method\"  for detecting texts that are generated by Large Language Models (LLMs).\nThe key idea behind their method is to revise the text to be detected using the ChatGPT model. The assumption is that if the text is  generated by an LLM, the ChatGPT model will make fewer revisions to it compared to human-written texts.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: Summary:
The reviewers consider the paper to present a simple and effective approach for detecting AI generated text. The main reviewers concerns however revolve around the models used in this paper (GPT-2 and GPT-3.5 turbo) for baseline comparisons, which makes log likelihood and similar measures unreliable for detecting machine-generated text. The absence of XSum, SQuAD, and WritingPrompts datasets, originally used to evaluate DetectGPT, raises questions about the comparability of the methods. Additionally, the study's heuristic approach may not work well when the generator and revision models are significantly different, like PALM or Claude models. Lastly, the study's binary classification approach may not address the challenge of accurately detecting machine-generated text, as it can be a difficult problem requiring higher accuracy. This could be considered a limitation of the approach.

The reviewers however seem to all agree that although the paper is strong it is not so exciting. I have read the reviewers comments and the authors rebuttal comments. Two of three reviewers conclude that the paper is a sound paper with soundness score of 4 while one gives a review of 3. All reviewers on the other hand give an ambivalent score on excitement. All reviewers commented on acknowledgement of rebuttals and made appropriate changes.

Reasons to Accept:
(1) The proposed method addresses the important task of detecting machine-generated text and presents promising results on various datasets.
(2) The approach is novel, simple, and effective, offering a valuable contribution to the field.
(3) The experiments involve multiple datasets and source models, demonstrating good generalization.

Reasons to Reject:
(1) Baseline comparisons might be flawed due to the use of different source models for generating text and calculating log likelihood, making the comparison less meaningful.
(2) The reliance on appendices for crucial information and the limitation of testing revision only with ChatGPT may hinder the paper's comprehensiveness.
Decision: Accept-Main