SeqXGPT: Sentence-Level AI-Generated Text Detection
Authors: Pengyu Wang
Linyang Li
Ke Ren
Botian Jiang
Dong Zhang
Xipeng Qiu
Venue: EMNLP 2023 Main (2023)
Keywords: AI generated text detection
security in NLP
LLM

Abstract: Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, it is important to build strong AI-generated text (AIGT) detectors. Current works only consider document-level AIGT detection, therefore, in this paper, we first introduce a sentence-level detection challenge by synthesizing a dataset that contains documents that are polished with LLMs, that is, the documents contain sentences written by humans and sentences modified by LLMs. Then we propose \textbf{Seq}uence \textbf{X} (Check) \textbf{GPT}, a novel method that utilizes log probability lists from white-box LLMs as features for sentence-level AIGT detection. These features are composed like \textit{waves} in speech processing and cannot be studied by LLMs. Therefore, we build SeqXGPT based on convolution and self-attention networks. We test it in both sentence and document-level detection challenges. Experimental results show that previous methods struggle in solving sentence-level AIGT detection, while our method not only significantly surpasses baseline methods in both sentence and document-level detection challenges but also exhibits strong generalization capabilities.

SeqXGPT: Sentence-Level AIGGenerated Text Detection
Pengyu Wang, Linyang Li; Ke Ren, Botian Jiang,
Dong Zhang, Xipeng Qiu
School of Computer Science, Fudan University
Shanghai Key Laboratory of Intelligent Information Processing, Fudan University (pywang22, kren22, btjiang23, dongzhang22 @m. fudan, edu. cn (linyangli19 xpqiu) fudan. edu.cn
Spoiler: Extreme Archery Archery also scems like a very good investment Not only enjoyable sport for enthusiasts, but also has practical uses bunting and self-delenne. With the popularity mov and games Reatrning archery. the demand for high-quality
Abstract
the rise.
Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, is important to build strong Al-generated text (AIGT) detectors. Current works only consider document-level AIGT detection, therefore, in this paper, we first introduce a sentence-level detection challenge by synthesizing a dataset that contains documents that are polished with LLMss, that is, documents contain sentences written by humans and sentences modified by LLMs. Then we propose Sequence X (Check) GPT, novel method that utilizes log probability lists from white-box LLMs as features for sentence-level AIGT detection. These features are composed like waves in speech processing and cannot be studied by LLMs. Therefore, we build SeqXGPT based convolution and self-attention networks. We test it in both sentence and document-level detection challenges. Experimental results show that previous methods struggle in solving sentence-level AIGT detection, while our method not only significantly surpasses baseline methods in both sentence and document-level detection challenges but also exhibits strong generalization capabilities.
White-Box Features
Word
SeqXGPT Prediction
Spoiler Extreme Archery Arehery also seems like a good investment has practical
Not only is an enjovable sport for enthusiasts, uses hunting and self-defense With the rise popularity of movies and
games featuring archery, the demand for high-quality gear rise.
Human OF AI
Figure 1: Process of Sentence-Level AIGT Detection Challenge. In this figure, only the first sentence of the candidate document is human-created, while the others are generated by AI (GPT-3.5-turbo) The deeper color, the higher the likelihood that this part AI- generated. The example demonstrates that SeqXGPT can efficiently conduct sentence-level AIGT detection.
solution is AIGT detection, which discriminates whether a given text is written or modified by AI system (Mitchell et al., 2023; Li al., 2023). Current AIGT detection strategies, such as supervised-learned discriminator 2 perplexity- based methods (Mitchell et al., 2023; Li et al., 2023), etc.. focus on discriminating whether whole document is generated by an Al. However, users often modify partial texts with LLMs rather than put full trust in LLMs to generate a whole document. Therefore, it is important to explore fine-grained (e.g. sentence-level) AIGT detection. Building methods that solve the sentence-level AIGT detection challenge is not an incremental modification over document-level AIGT detection. On the hand, model-wise methods like DetectGPT and Sniffer require rather long
1 Introduction
With the rapid growth of Large Language Models (LLMs) exemplified by PaLM, ChatGPT and
GPT-4 (Chowdhery et al., 2022: OpenAl, 2022, 2023), which are derived from pre-trained models (PTMs) (Devlin et al., 2018; Radford al., 2019; Lewis al., 2019; Raffel al., 2020), Al-generated texts (AIGT) can be seen almost everywhere. Therefore, avoiding the abuse of AIGT a significant challenge that require great effort from NLP researchers. One effective
Advisor.
Corresponding author.
Our code and generated datasets are availahle at https: //g //gituuk thu.. com/ Jihual -wpy/ /PQ
httb:///ggthhhhottttth. gpt - 2-outouuuut....m dataset.

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document input (over 100 tokens), making them less effectiive at recognizing short sentences. On the other hand, supervised methods such as RoBERTa fine-tuning, are prone to overfitting on the training data, as highlighted by Mitchell et al. (2023); Li et al. (2023). Therefore, it is necessary to build sentence-level detectors considering the limitation of current AIGT methods.
to ensure security and trustworthiness of LLM applications.
2.1 Principal Approaches to AIGT Detection Broadly, AIGT detection methodologies fall into two primary categories:
Supervised Learning: This involves training models on labeled datasets to distinguish between human and Al-generated texts. Notable efforts in this domain include the studies by Bakhtin et al. (2019); Solaiman et al. (2019b); Uchendu et al. (2020), where supervised models were trained on the top of neural representations. OpenAl releases GPT-2 outputs along with a supervised- learning baseline based on RoBERTa, which claims that GPT-2 outputs are complicated for pre-trained models to discriminate.
In this paper, we study the sentence-level AIGT detection challenge:
We first build sentence-level AIGT detection dataset that contains documents with both human- written sentences and Al-generated sentences, which are more likely occur in real-world AI- assisted writings. Further, we introduce GPT, a strong approach to solve the proposed challenge. Since previous works (Solaiman et al., 2019a; Mitchell et al., 2023; Li et al., 2023) show that perplexity is significant feature to be used for AIGT detection, we extract word-wise log probability lists from white-box models as foundational features, as seen Figure 1. These temporal features are composed like waves in speech processing where convolution networks are often used (Baevski et al., 2020; Zhang et al., 2023). Therefore, we also use convolution network, followed by self-attention (Vaswani et al., 2017) layers, to process the wave-like features. We employ sequence labeling approach to train SeqXGPT, and select the most frequent word-wise category as the sentence category. In this way, we are able analyze the fine-graine -reeegraiedd sentence-level text provenance.
Utilizing Model-wise Features: Rather than relying solely on labeled datasets, this approach focuses on intrinsic features texts, such as token log probabilities, token ranks, and predictive entropy. as proposed by Gehrmann al. (2019); Solaiman et al. (2019b); Ippolito al. (2020). The original version of GPTZero attempts to use the text perplexities as white-bon features, while DetectGPT (Mitchell et al., 2023) innovated method leveraging
a perturbation- average per-token log probabilities. The recent work of Li et al. (2023) extends this further by studying multi-model detection through text perplexities, aiming to trace the precise LLM origin of texts.
We conduct experiments based on the synthesized dataset and test modified existing methods as well as proposed SeqXGPT accordingly. E'xpeimennal results show that existing methods, such as DetectGPT and Sniffer, fail to solve sentence-level AIGT detection. Our proposed SeqXGPT not only obtains promising results in both sentence and document-level AIGT detection challenges, but also exhibits excellent generalization on out-of-distributie datasets.
2.2 Detection Task Formulations
The AIGT detection tasks can be formulated in different setups:
Particular-Model Binary AIGT Detection:
this setup, the objective to discriminate whether a text was produced by a specific known AI model or by a human. Both GPTZero and DetectGPT fall into this category.
2 Background
The proliferation of Large Language Models (LLMs) such GPT-4 has given rise increased concerns about the potential misuse of Al- generated texts (AIGT) (Brown et al., 2020; Zhang et al., 2022; Scao et al., 2022). This emphasizes the necessity for robust detection mechanisms
Mixed-Model Binary AIGT Detection: Here, the detectors are designed to identify Al-generated content without the need to pinpoint the exact model of origin.

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3. Mixed-Model Multiclass AIGT Detection: This more difficult task, aiming only to detect AIGT but also to identify the exact Al model behind it. Sniffer (Li et al., 2023) is prominent model in this category.
3.2 Dataset Construction
Current AIGT detection datasets are mainly designed for document-level detection. Therefore, we synthesize sentence-level detection dataset for the study of fine-grained AIGT detection.
Since the human-authored documents from SnifferBench cover various domains including news articles from XSum (Narayan 2018), social media posts from IMDB (Maas et al., 2011), web texts (Radford et al., 2019), scientific articles from PubMed and Arxiv(Cohan et al., 2018), and technical documentation from SQuAD (Rajpurkar et al., 2016), we utilize these documents to construct content that include both human-authored authored and Al-generated sentences. To human
2.3 Related Fields of Study
It's worth noting that AIGT detection has parallels with other security-centric studies such as watermarking (Kurita et al., 2020; Li et al., 2021; Kirchenbauer et al., 2023), harmful AI (Bai et al., 2022), and adversarial attacks (Jin et al., 2019; Li et al., 2020). These studies, together with AIGT detection, are indispensable for utilizing and safeguarding these advanced technologies and becoming more important in the era of LLMss.
al.,
ensure diversity while preserving coherence, we randomly select the first third sentences from the numan-generated documents as the prompt for subsequent Al sentence generation.
3 Sentence-Level AIGT Detection
3.1 Sentence-Level Detection
Specifically, when generating the Al component of document using language models such GPT- 2, utilize the prompt obtained via the above process get the corresponding Al-generated sentences. the case of instruction-tunet models like GPT-3.5-turbo, we provide specific instruction to assure the generation. We show instruction details in Appendix D.
The document-level detection methods struggle to accurately evaluate documents containing a mix of Al-generated and human-authored content or consisting of few sentences, which can potentially lead to higher rate of false negatives or false positives.
In response this limitation, we aim to study sentence-level AIGT detection. Sentence-level AIGT detection offers a fine-grain auc-grrined text analysis compared to document-level AIGT detection, as it explores each sentence within the entire text. By addressing this challenge, we could significantly reduce the risk of misidentification and achieve both higher detection accuracy and finer detection granularity than document-leved detection.
Regarding open-source models, gather the Al-generated portions of document from GPT-2 (Radford et al., 2019), GPT-J (Wang and Komatsuzaki, 2021), GPT-Neo (Black et al., 2022), and LLaMA (Touvron et al., 2023). For models which provide only partial access, we collect data from GPT-3.5-turo, model. With collection of 6,000 human- documents from SnifferBench, assemble 6,000 documents containing both human- written and Al-generated sentences from each of the models. This results total of 30,000 documents. Accordingly, we have:
instruction-tuned
Similarly, we study sentence-level detection challenges as follows:
similarly
1. Particular-Mode! Binary AIGT Detection: Discriminate whether each sentence within a candidate document is generated by a specific model or by human.
Particular-Model Binary Detection Dataset To contrast with DetectGPT and the log p(x) method, we construct the dataset using documents that consist of sentences generated by particular model or by humans. For each document, sentences generated by the particular model are labeled as "AI", while sentences created by humans are labeled as "human". Using the collected data, we construct five binary detection datasets in total. Mixed-Model Binary Detection Dataset We utilize the collected data to construct a dataset
2. Mixed-Model Binary AIGT Detection: Discriminate whether each sentence is generated by an AI model or a human, regardless of which AI model it is.
3. Mixed-Model Multiclass AIGT Detection: Discriminate whether each sentence is generated by an AI model or a human and identify which AI model generates it.

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XX
Classifier
Classiifer
Classifier
Classifier
220
50
(a)
(b)
(c)
Figure 2: Strategies for AIGT Detection. (a) Document-Level AIGT Detection: Determine the category based on the entire candidate document. (b) Sentence Classification for Sentence-Level AIGT Detection: Classify each sentence one by one using the same model. (c) Sequence Labeling for Sentence-Level AIGT Detection: Classify the label for each word, then select the most frequently occurring category as the final category for each sentence.
intended for multi-model binary detection research. For each document, we label sentences generated by any model as "Al", and sentences created by humans as "human".
sequence labeling tasks (Huang et al., 2015; Wang and Ren, 2022). We use B-AI, B-HUMAN, etc. as word-wise lahels.
Mixed-Model Multiclass Detection Dataset For multiclass detection research, we again use the entire data. Unlike the binary detection dataset that merely differentiates between human-created and Al-generated sentences, we use more specific labels such as "GPT2", "GPTNeo", and so forth, instead of the generic label "AI". This allows us to further distinguish among a variety of AI models. For each dataset, We divide the documents into train/test split with a 90%/10% partition. We name the collected datasets SegXGPT-Bench, which can further used for fine-grained AIGT detection.
4.2 Baseline Approaches
In the sentence-level AIGT detection challenge, we explore how to modify basclines used in document- level detection:
log p(z): We consider sentence-level detection as sentence classification task to implement the log p(æ) method. The log p(z) method discriminates AIGT through perplexity. Specifically, utilize particular model such GPT-2 compute the perplexity of each sentence in the document and draw histogram (Figure to show the distribution of perplexity. Then we select a threshold manually as the discrimination boundary to determine whether each sentence is generated by a human or an Al.
4 SeqXGPT
To solve the sentence-level detection challenge, we introduce strong method SeqXGPT:
DetectGPT (Mitchell et al., 2023): Similarly, we regard sentence-level detection as sentence classification task to implement DetectGPT. DetectGPT discriminates AIGT using their proposed z-score. For each sentence, we add multiple perturbations (we try 40 perturbations for each sentence), We then compute the z-scores for each sentence based particular model and generate histogram (Figure 4) showing the score distribution. Similarly, we manually select a threshold to discriminate Al-generated sentences.
4.1 Strategies for Sentence-Level Detection
A sentence-level detection task is a specific form of the sentence classification task. Therefore, there are mainly two approaches:
Sentence Classification This method treats each sentence the document as an input text and independently determines whether each sentence is generated by AI, as depicted in Figure 2(b).
Sequence Labeling This method treats the entire document as input, creating a sequence labeling task to decide the label for each word. For each sentence, we count the number of times each label appears and select the most frequent label as the category of the sentence, as shown in Figure 2(c). It's worth noting that each dataset uses a cross- label set based on (B, 1, O}, similar to the usage in
Sniffer (Li et al., 2023): Sniffer is a powerful model capable of detecting and tracing the origins of Al-generated texts. In order conduct sentence- level AIGT detection, we train sentence-level Sniffer following the structure and training process of the original Sniffer, except that our input is a single sentence instead of an entire document.

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Werk-Leve Labels
Yruk
Lincer Claveifiction Layer
2019). SentencePiece by LLaMA (Touvron et al., 2023)), they do not align perfectly. As result, we align tokens to words, which addresses potential discrepancies in the tokenization process across different LLMs. We use a general word-wise tokenization of S: w = -wi,.... wi] and align calculated log probabilities of tokens in æ to the words in Therefore, we can obtain list of word-wise log probabilities Ug. (w) from token- wise log probability list lle (z). We concatenate these word-wise log probability lists together form the foundational features where the feature vector of the 7th word w, is 4 = [Ule (wx))...,ulp (w:)).
Comestuaized
Foswer Enjader
Transformer
aaimt Repersemations
Foundational Featima
Figure 3: SeqXGPT Framework.
[4..... 4]..
RoBERTa: RoBERTa (Liu et al., 2019) is based on the Transformer encoder that can perform sentence classification tasks as well as sequence labeling tasks. Based on RoBERTa-base we train two RoBERTa baselines for sentence-level AIGT detection. As mentioned in Sec. 4.1, one model employs sequence labeling approach, while the other uses sentence classification method, They are referred as Seq-RoBERTa and Sent- RoBERTa, respectively.
We select GPT2, GPT-Neo, GPT-J, and LLaMA as white-box models to obtain the foundational features. In the various implementations of alignment algorithms, this paper utilizes the alignment method proposed by Li et al. (2023).
Feature Encoder
The foundational features lists of word-wiss log probabilities, which prevents us from utilizing any existing pre-trained models. Consequently, we need to design a new, efficient model structure to perform sequence labeling for AIGT detection based on these foundational features.
4.3 SeqXGPT Framework
In this section, we will provide a detailed introduction to SeqXGPT. The SeqXGPT employs a sequence labeling approach for sentence- level AIGT detection, which consists of the following three parts: (1) Perplexity Extraction and Alignment; (2) Feature Encoder; (3) Linear Classification Layer.
The word-wise log probability lists for the same sentence of different LLMs may differ due to different parameter scales, training data, and some other factors. Here, we propose treating the word-wise log probability list as kind of feature that reflects model's understanding of diffcrent semantic and syntactic structures. As an example, more complex models are able to learn more complex language patterns and syntactic structures, which can result in higher probability scores. Moreover, the actual log probability list might be subject to some uncertainty due sampling randomness and other factors. Duc to these properties, it is possible view these temporal lists as waves in speech signals. Inspired by speech processing studies, we choose to employ convolutional networks to handle these foundational features. Using convolutional networks. local features can be extracted from input sequences and mapped into hidden feature space. Subsequently, the output features are then fed into context network based on self-attention layers, which can capture long-range dependencies to obtain contextualized features. In this way, our model can better understand and process the
Perplexity Extraction and Alignment
Previous works demonstrate that both the average per-token log probability (DetectGPT) and contrastive features derived from token-wise probabilities (Sniffer) can contribute AIGT detection. Therefore, in this work, we choose to extract token-wise log probability lists from several public open-source models, serving as the original features of SeqXGPT.
Specifically, given candidate text S, known model O, and the corresponding tokenizer Tn, obtain the encoded tokens x [ 24, and a list of token-wise log probabilities Uo, (æ), where the log probability of the 5th token æ; is the log- likelihood 14g, (z:) = logpe, (x)( Given a list of known models .,.... UN. we can obtain N token- wise log probability lists of the same text S. Considering that the tokens are commonly sub- words of different granularities and that different tokenizers have different tokenization methods (e.g. byte-level BPE by GPT2 (Radford et al.,

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wave-like foundational features.
5.2 Sentence-Level Detection Results
As shown in Figure 3, the foundational features are first fed into five-layer convolutional network f L 2 to obtain the latent representations [=1,..., 21]. Then, apply context network 9 2 + C to the output of the convolutional network build contextualized features (e capturing information from the entire sequence. The convolutional network has kernel sizes (5, 3, 3,3, 3) and strides (1, 1,1,1,1) while the context network has two Transformer layers with the simple fixed positional embedding. More implementation details can be seen in Appendix A.
In this section, we present three groups of experiments:
Particular-Model Binary AIGT Detection We test zero-shot methods log p(æ) and DetectGPT, well Sent-RofP RonTRRaERTTa and our SeqXGPT two datasets shown in Table 1. The results for the other two datasets can be found the appendix Table
6.
Model
0.6
0.6
0.4
0.4
Linear Classification Layer
After extracting the contextualized features, we train a simple linear classifier F(-) project the features of each word to different labels. Finally, we count the number of times each word label appears and select the most frequent label as the final category of each sentence.
0.2
0.2
120.0
2.0
4.0
0.0
2.0
3.0
Frequency
GPT2 Perplexity Range
GPT--Ne Perplexity Range
2.0
1.5
1.0
5 Experiments
0.5
5.1 Implementation Details
0.0
DeleatGPTGGPT2 Range
DelecIGPT(G/T-Nee) Range
We conduct sentence-level AIGT detection based on SegXGPT-Bench. As part of our experiments, we test modified existing methods deseribed in Sec. 4.2, well as our proposed SeqXGPT detailed in Sec. 4.3.
Figure 4: The discrepancy between Al-generated and human-created sentences. In each figure, different bars show different sentence origins and each figure is to use a certain model of a certain detect method to test given sentences. The top two figures log p(x), while the bottom two use DetectGPT.
We use four open-source (L)LMs to construct our SenXG GPT2-xl (L.5B), GPT-Neo (2.7B), GPT-J (6B) and LLaMA (7B). These models are also utilized to extract perplexity lists. which are used to construct the original features our SeqXGPT and the contrastive features for Sniffer. For each model, we set up an inference server on NVIDIA4090 GPUs specifically for extracting perplexity lists. The maximum sequence length is set to 1024 for all models, which is the maximum context length supported by all the pre-trained models we used. We align all texts with a white-space tokenizer to obtain uniform tokenizations for our SeqXGPT.
As seen in Figure there large overlap between the peaks of Al-generated and human- created sentences when using log p(æ) and DetectGPT, making it difficult to distinguish sentences from different categories. The peaks almost completely overlap for DetectGPT in particular. This situation can be attributed to two main factors. Firstly, the perplexity of sentence tends to be more sensitive than document. Secondly, given the brevity of sentences, perturbations often fail to achieve the same effect as in DetectGPT. That is, the perturbed sentences either remain nearly identical to the original, or vary significantly. These findings indicate that these two zero-shot methods are unsuitable for sentence-level AIGT detection.
In the evaluation process, we utilize Precision (P.), Recall (R.), and Macro-Fl Score metries. Precision and Recall respectively reflect the "aceuracy" and "coverage" of each category, while the Macro-F1 Score effectively combines these two indicators, allowing us to consider the overall performance.
Overall, SeqXGPT exhibits great performance on all datasets, including the two presented

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Different AIGT Origins
GPT-2
GPT-Neo
Method
P.(AT)
R.(AT)
P.(H.)
R.(H.)
Macro-F1
P((A))
R.(AT)
P.HH.)
R.(H.)
Macro-F1
log p(æ)
82.2
74.9
43.1
53.9
63.1
81.2
67.8
34.2
51.7
57.5
DetectGPT
80.9
55.4
32.7
62.4
54.3
82.6
44.2
29.1
71.2
49.4
Sent-RoBLKiaa
96.9
66.5
84.4
95.6
82.7
66.0
83.0
SeqXGPT 99.3
97.9
94.5
97.1
97.2
99.5
98.2
94.8
98.1
97.6
Table 1: Results of Particular-Model Binary AIGT Detection on two datasets with Al Al-geeerated sentences are from GPT-2 and GPT-Neo, respectively. The Macro-F1 used to measure the overall performance, while the (precision) and R. (recall) are used to measure the performance in specific category.
Different AIGT Origins
GPT-2
GPT-2-Neo
GPT-J
LLaMA
GPT-3
Human
Method
R
P.
R.
R.
P.
R.
P.
R.
R.
Macro-Fi
Sniffer
47.5
56.3
48.4
42.9
39.0
33.5
41.8
16.0
52.8
55.4
51.2
67.2
44.7
Sent- RoBERTa
36.9
27.6
34,9
57.5
33.6
65.5
97.1
89.4
$2.9
Seq RoBERTa
42.1
81.4
45.3
30.9
61.6
21.6
75.5
82.0
90.3
98.9
94,6
90.1
64.9
SeqXGPT
99.2
97.9
99.3
98.2
97.6
96.8
95.8
90.8
94.1
93.7
90.7
95.2
95.7
Transformer
92.4
92.7
88.9
93.3
62.1
82.1
14.3
22.7
0.2
42.0
95.7
56.9
CNN
0.0
0.0
100.0
Table Results of Mixed-Model Multiclass AIGC Detection.
Table and the other two in Table 6. Despite Sent-RoBERTa performs better than two zero-shot methods, it is still far inferior to method.
Mixed AIGT Origins
Method
P(AT)
R.(AI)
P.H..)
R.(I1)
Macro-F1
Snifter
83.2
92.7
67.8
45.3
71.0
Sent RoBERTs
97.3
97.9
93.5
91.8
95.1
Moded-Model Binary AIGT Detection We implement Sniffer and Sent-RoBERTa based on sentence classification tasks, as well as our SeqXGPT and Seq- RoBERTa based sequence labeling tasks. From the results in Table 3, it is apparent that SeqXGPT shows the best performance among these four methods, significantly demonstrating the effectiveness of our method. In contrast, the performance of Sniffer is noticeably inferior, which emphasizes that document-level AIGT detection methods cannot be effectively modified for sentence- level AIGT detection. Interestingly, we find that the performance of both RoBERTa-based methods is slightly inferior to SeqXGPT in overall performance. This suggests that the semantic features of RoBERTa might be helpful to discriminate human-created sentences.
Soq-RoBERTa SeqXGPT
96.4
98.5
95.0
88.9
94.6
98.2
97.1
91.4
94.5
95.3
Table 3: Detection.
Results of Mixed-Model Binary AIGT
sentences generated by humans more human- like LLMs, such as GPT-3.5-turbo, while having difficulty detecting sentences generated by GPT-2/GPT-//Neo. The reason for this may be that sentences generated by stronger models contain more semantic features. This is consistent with the findings of Li et al. (2023). In contrast, the Req-RoBERTa outperforms Sent-RoBERTa, indicating that sequence labeling methods, with their ability to obtain the context information of the entire document, are more suitable for sentence-level AIGT detection. Sniffer, however, performs poorly, which may be due to its primary design focusing on document-level detection.
Mixed-Model Multiclass AIGT Detection As illustrated in Table 2, SeqXGPT can accurately discriminate sentences generated by various models and those authored by humans, demonstrating its strength multi-class detection. It is noteworthy that RoBeRTa-based methods perform significantly worse than binary AIGT detection. They are better at discriminating
5.3 Document-Leevel Detection Results
We conduct experiments on document-level detection dataset (more details in Appendix C. For sentence classification based models, we classify each sentence and choose the category

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Different AIGT Origins
GPT-2
GPT-2-Neo
GPT-J
LLaMA
GPT-3
Human
Method
R.
P.
R.
R.
P.
R.
R.
P.
R.
Macro-F1
Sniffer
76.5
96.6
86.0
83.1
75.0
74.2
92.9
7.0
79.5
83.1
53.4
87.2
67.5
Sent-RoBEKTa
45.2
73.0
39.7
46.3
28.3
21.5
72.4
10.5
73.4
100.0
97.4
92.0
53.4
Seq RoBRRTa
50.4
85.5
42.1
40.0
42.4
26.5
62.6
72.0
85.7
85.9
36.5
57.9
SeqXGPT
100.0
99.0
100.0
99.0
99.5
96.5
96.8
90.0
94.5
86.5
77.6
93.5
94.2
Table 4: Results of Document-Level AIGT Detection.
Different AIGT Origins
GPT-2
GPT-2-Neo
GPT-J
LLaMA
GPT-3
Human
Method
R.
R.
P.
R.
P
R.
P
R.
P.
R.
Macro-Fi
Sniffer
80.0
44.1
21.5
54,9
73.0
53.7
60.0
RoBERa
30.3
35.0
13.6
27.4
24.3
25.3
35.1
27.5
61.7
94.4
75.1
19.1
35.2
Seq RoBERTa
64.2
40.1
60.7
74.5
75.9
86.2
99.3
78.4
60.6
SeqXGPT
99.5
98.4
99.1
83.6
95.5
95.0
91.6
89.1
96.5
91.0
83.1
94.0
92.8
Table 5: Results of
Sentence-Level AIGT Detection.
with the highest frequency the document's category. For sequence labeling- models, we label each word and select the category with the greatest number of appearances as the document's category.
strong generalization capabilities. Conversely, Sent-RoBERTa shows a significant decline performance, especially when discriminating sentences generated by GPT-3.5-turbe and humans. This observation aligns with the findings from prior studies (Mitchell et al., 2023; Li et al., 2023), suggesting that semantically- methods prone overfitting the training dataset. However, Req-RoBERTa performs relatively better on OOD data. We believe that this is because sequence labeling methods can capture contextualized information, helping to mitigate overfitting during learning. Despite this, its performance still falls short of SeqXGPT.
Table 4 indicates that sentence-leve detection methods can be transformed and directly applied document-level detection, and the performance positively correlated with their performance on sentence-level detection. Despite their great performance in detecting human-generated sentences, both Seq-RoBERTa and SeqXGPT perform slightly worse in discriminating human- created documents. This is mainly because our training set for human-created data only comes from the first 1-3 sentences, leading shortage of learning samples. Therefore, appropriately increasing human- suman-generated data can further enhance the performance. We can see that Sniffer performs much better in document-level detection, indicating that Sniffer is more suitable for this task. Overall, SeqXGPT exhibits excellent performance in document-level detection.
5.5 Ablations and Analysis
In this section, we perform ablation experiments on the structure of SeqXGPT to verify the effectiveness of different structures in the model.
First, we train model using only transformer layers. As shown in Table 2, this model struggles with AIGT detection, incorrectly classifying all sentences as human-written. Although there are differences between a set of input feature waves (foundational features), they are highly correlated, often having the same rise and fall trend (Figure 3). Further, these features only have 4 dimensions. We think it's the high correlation and sparsity of the foundational features make it difficult for the Transformer layers to effectively learn AIGT detection. In contrast, CNNs are very good at handling temporal features. When combined
5.4 Out-of-Distributtio Results
Previous methods often show limitations on out-of- distribution (OOD) datasets (Mitchell et al., 2023). Therefore, we intend to test the performance of each method the OOD sentence-level detection dataset (more details in Appendix C.2).
Table 5 demonstrates the great performance SeqXGPT on OOD data, reflecting its

---

with CNNs, it's easy to extract more effective. concentrated feature vectors from these feature waves, thereby providing higher-quality features for subsequent Transformer layers. Moreover, the parameter-sharing mechanism of CNNs can reduce the parameters, helping us to learn effective features from limited samples. Thus, CNNs play an important role in SeqXGPT.
In future studies, we aim explore more complex scenarios where a document contains sentences from multiple sources.
Acknowledgements
We would like to extend gratitude to the anonymous reviewers for their valuable comments. Additionally, we sincerely thank Qipeng Guo for his valuable discussions and insightful suggestions on this study. This work was supported by the National Natural Science Foundation of China (No. 62236004 and No. 62022027).
However, when only using CNNs (without Transformer layers), the model performs poorly on sentences generated by strong models such as GPT-3.5-turbo. The reason for this may be that CNNs primarily extract local features. In dealing with sentences generated by these strong models, it is not sufficient to rely solely on local features, and we must also consider the contextualized features provided by the Transformer layers.
References
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. 2020. wav2vec 2.0: A framework for self-supervised learning of speech representations. Advances neural information processing systems, 33: 12449-1246660 12460.
6 Conclusion
In this paper, we first introduce the challenge of sentence-level AIGT detection and propose three tasks based on existing research in AIGT detection. Further, we introduce strong approach. SeqXGPT, as well as a benchmark to solve this challenge. Through extensive experiments, our proposed SeqXGPT can obtain promising results in both sentence and document-level AIGT detection. On the OOD testset, SeqXGPT also exhibits strong generalization. We hope that SeqXGPT will inspire future research in AIGT detection, and may also provide insightful references for the detection of content generated by models in other fields.
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma. Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, al. 2022. Training helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204. 05862.
Anton Bakhtin, Sam Gross, Myle Out, Yuntian Deng. Marc' Aurelio Ranzato, and Arthur D. Szlam. 2019. Real fake? learning discriminate machine from human generated text. ArXiv, Abo/1990.03351.
Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao. Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, al. 2022. Gpt-neox-20b. An open spen-source autoregressive language model. arXiv preprint arXiv:2204.06745
Limitations
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neclakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901.
Despite ScqXGPT exhibits excellent performance in both sentence and document-level AIGT detection challenge and exhibits strong generalization, it still present certain limitations:
(1) We did not incorporate semantic features, which could potentially assist our model further in the sentence recognition process, particularly in cases involving human-like sentence generation. We leave this exploration for future work.
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sehastian Gehrmann, et al. 2022. Palm: Scaling language modeling with pathways. arXiv preprint wxXiv2204.00211.
(2) During the construction of GPT-3.5-turbo data, we did not extensively investigate the impact of more diversified instructions. Future research will dive into exploring the influence of instructions on AIGT detection.
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A discourse-aware attention model for abstractive summarization of long documents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers),
(3) Due to limitations imposed by the model's context length and generation patterns, our samples only consist of two distinct sources of sentences.

---

pages 615-621, New Orleans, Louisiana. Association for Computational Linguistics.
Linyang Li, Pengyu Wang. Ke Ren, Tianxiang Sun, and Xipeng Qiu. 2023. Origin tracing and detecting of llms. arXiv preprint arXiv:2304. 230...422..
Jacob Devlin. Ming-Wor Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: pre-training of deep bidirectional transformers for language understanding. CORR, abs/1810.04805.
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907. 197.7111 1692.
Sebastian Gchrmann, Hendrik Strobelt, and Alexander Rush. 2019. GLTR: Statistical detection and visualization of generated text. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguisties: System Demonstrations, pages 111-116, Florence, Italy. Association for Computational Linguistics.
Andrew Maas, Raymond Daly, Peter T Pham, Dan Huang, Andrew Y Ng. and Christopher Potts. 2011. Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies, pages 142-150.
Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional lstm-crf models for sequence tagging. arXiv preprint urXiv:1508.0199
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher Mamning, and Chelsea Finn. 2023. Detectgpt: Zero-shot machine-generated text detection using probability curvature. ArXiv, abs/2301.11305.
Daphne Ippolito, Daniel Duckworth, Chris Callison- Burch, and Douglas Eck. 2020. Automatic detection of generated text is casiest when humans are fooled. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguisticx, pages I 1888-1822, Online. Association for Computational Linguistics.
Shashi Narayan, Shay Cohen, and Mirella Lapata. 2018. Don't give me the details, just the summary! Topic-aware convolutional neural networks for extreme summarization.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2019. Is BERT really robust? natural language attack on text classification and entailment. CoRR, 1bs/1907.
OpenAl. 2022.
Chatgpt: Optimizing language models for dialogue. http:/ /wb. archive. orgg/ web 2020/ 09000707/httetttoe /tttttss:://ppeeee http///ppaaa..ccm com/ b blog/chattttt//// Accessed: 2023-01-10.
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint /7055 03551.
OpenAl. 2023. Gpt-4 technical report. ArXiv, abs/2303.08774.
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, lan Miers, and Tom Goldstein. 2023. A watermark for large language models. arXiv preprint ar3ir:2301 2302210226........
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, lly Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAl blog, 188):
Keita Kurita, Paul Michel, and Graham Neubig. 2020. Weight poisoning attacks on pretrained models. In Proceedings of the 58th Annual Meeting of the Association for Compututional Linguistics, pages 2793-2806, Online. Association for Computational Linguistics.
Colin Raffel, Noam Shazeer, Adam Roberts. Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with unified text-to- text transformer. The Journal of Machine Learning Research, 21()))5485-5551
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zeulemoyer. 2019. Bart: Denoising sequence-to sequence pre- pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv: 13461.
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arxiv:106.005200
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, al. 2022. Bloom: A 176b- parameter open-access multilingual language model. arXiv preprint arX
Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, and Xipeng Qiu. 2020. Bert-attack: Adversarial attack against bert using bert. arXiv preprint arXiv:2004.0940 09984.
Linyang Li. Demin Song, Xiaonan Li. Jiehang Zeng. Ruotian Ma, and Xipeng Qiu. 2021. Backdoor attacks pre-trained models by layerwise weight poisoning. Conference on Empirical Methods in Natural Language Processing.
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert- Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps. et al. 2019a. Release strategies and the social

---

impacts of language models. arXiv:1908.09200
arXiv preprint
Trene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, and Jasmine Wang. 2019b. Release strategies and the social impacts of language models. ArXiv, abs/1908.09203.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Maree-Aune Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13/) 13971.
Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee. 2020. Authorship attribution for neural text generation. In Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8384-88883 8395, Online. Association for Computational Linguistics.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Eukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances neural information processing systems, pages 0999-6008..
Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https:///ithone com/kingotlolz mesh-transformerz jax.
Pengyu Wang and Zhichen Ren. 2022. The uncertainty- based retrieval framework for ancient chinese cws and pos. Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages, pages 164-168. 168.
Dong Zhang, Rong Ye, Tom Ko, Mingxuan Wang. and Yaqian Zhou. 2023. Dub: Discrete unit back- translation for speech translation. In Findings of ACI.
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2005.01066

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Different AIGT Origins
GPT-J
LLaMA
Method
P((A))
R.(AT)
P.(H.)
R.(H.)
Macro-F1
P.(A))
R.(AT)
P.H..)
R.(H.)
Macro-F1
log p(æ)
80.2
73.2
30.5
39.4
55.5
70.2
68.1
26.1
28.1
48.1
DetectGPT
81.0
56.8
27.7
55.5
51.9
84.1
38.5
33.7
81.2
50.2
SenI-RoBLKIia
96.7
84.7
62.3
82.4
92.7
77.0
63.5
79.6
SeqXGPT
98.0
97.8
92.6
93.3
95.4
97.3
94.7
87.0
93.1
92.9
Table 6: Results of Particular-Model Binary AIGT Detection on two datasets with Al Al-generated sentences are from GPT-J and LLaMA, respectively.
A Implementation Details
C.1 Document-Level Detection Dataset
In order evaluate the performance in document- level AIGT detection, we construct document- level detection dataset. To this end, we randomly sampled 200 human-created documents from the test set of SegXGPT-Bench, which are used to construct Al-generated documents. Similar to Mitchell et al. (2023) and Li et al. (2023), we use the first sentence prompt to generate the entire document. This process essentially the same the construction process described in Sec. 3.2. The significant difference that we set the maximum sequence length as large as possible and remove the prompt part after generation. We mix documents created by humans with those generated by Al to form our document-level detection dataset.
In this section, we will discuss the implementation details of the Feature Encoder, which is composed of convolution network and context network.
We use five-layer convolution network, where the sizes of the convolution kernels are (3,3,333,3, the strides are (1,1,1,1.1), and the number of output channels are (64, 128, 12,,,12,,41 128,64). Since XGP is trained using sequence labelling, the input and output must have the same length. We specify padding= 'aaee' in torch.un. Convld to ensure that the lengths of the foundational features and latent representations are identical, despite the fact that the dimensions of each feature vector may differ. Our Context Network consists of two transformer layers with 16 attention heads and hidden size of 512. In implementation, only use the simplest absolute position encoding as Vaswani et (2017).
C.2 OOD Sentence-Level Detection Dataset
We construct OOD sentence-level detection datasel to evaluate the performance of various methods on OOD data. Our aim in creating this dataset ensure both topic-wise and format-wise diversity to best represent the possible challenges faced by Al-generated text detectors. With this goal in mind, we utilize the triviaQA dataset (Joshi et al.. 2017) as the foundational data for our OOD dataset. This choice was made after careful consideration and deliberation.
More Results
We provided results of Particular-Model Binary AIGT Detection on the other two datasets in Table 6. In fact, we have five binary detection datasets in total. Since Sniffer and DetectGPT can only perform white-box detection, we did not conduct experiment on dataset with Al-generated sentences are from GPT-3.5-turbo.
TriviaQA covers vast array of topics, drawing from diverse collection of sources including blog articles, news reports, encyclopedia entries, and Wikipedia pages. This diversity ensures a comprehensive coverage of topics and introduces a rich variety of text styles and formats. Moreover, significant advantage of utilizing triviaQA is its publication timeline. Published prior to the widespread emergence of Al text-generation technologies, the dataset less likely to have been influenced by Al-generated texts, ensuring the authenticity of its numan-deriven content. For the OOD dataset construction, we randomly
Construction of Supplementary Datasets
In addition to the SeqXGPT-Bench designed specifically for sentence-level AIGT detection, we have also developed two additional datasets. These two datasets are respectively constructed for document-level AIGT detection and Out- of-Distribution (OOD) sentence-level AIGT detection. In the following, we will claborate on the construction process of these two datasets.

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sample 200 evidence documents from triviaQA. These documents, with their wide-ranging domains and inherent diversity, form an excellent basis for testing OOD performance. The methodology adopted for constructing this dataset mirrors that detailed in Sec. 3.2. where we randomly select the first to third sentences from the human-generated documents as the prompt for subsequent AI generation. We then employ this newly constructed dataset as a testbed for evaluating the OOD performance of the models trained in Sec. 5.2.
Instruction Details
The specific instruction used in GPT-3.5-turbe is: Please provide continuation for the following content to make it coherent: (prompt)
We consider simple instruction since we focus on studying the sentence-level AIGT detection without further analyzing the working mechanisms and better usage of instructions, which we leave to future works.


Reviews:
[{"review_id": "pHSePBU55u", "reviewer": "Reviewer_8nB6", "summary": "", "questions": "A. Would there be any other ways to create the dataset, other than seeding the human-generated sentence in the beginning and then using different LMs to fill up the rest?  My worries are related to possible systematic bias of the dataset due to how the dataset is created --- monotonically increasing tendency to be more likely to be AI-generated as more sentences appear? Would you consider your proposed SeqXGPT-Bench is guarded against this kind of bias? \n\nB. From a similar vein, how wide or extensive does the currently evaluated OOD dataset cover the possibilities of AI-generated texts? This may be topic-wise or format-wise. I raise this point from the perspective of a potentially interested human being to implement this type of detector for a similar use case, and I would be greatly appreciative if this approach would work like a charm in my use case.", "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 sincerely appreciate your valuable comments!\n\n\nAbout Weakness #1:\n\nPlease see Question A and Question B.\n\n\nAbout Question A:\n\nOur dataset construction method is based on prior work like DetectGPT and Sniffer, but with modifications. \n\nOn the one hand, compared to DetectGPT, SeqXGPT-Bench covers various domains including news articles, social media posts, web texts, scientific articles, and technical documentation. Therefore, SeqXGPT-Bench is not narrow and its domain bias is relatively low.\n\nOn the other hand, we've incorporated a randomization mechanism that allows for a random number of human-generated sentences in each document. This helps mitigate the risk of systematic bias that might arise from this data generation approach.\n\n\nAbout Question B:\n\nWe fully understand your concern about the breadth of OOD dataset, and we appreciate the opportunity to clarify our considerations and selections when constructing the OOD dataset.\n\nFirstly, our Out-of-Distribution (OOD) dataset is built upon the triviaQA dataset, which was selected after careful deliberation. The triviaQA dataset encompasses a wide array of topics and derives from varied sources, such as blog articles, news reports, encyclopedia entries, and Wikipedia pages. This not only provides a wide and comprehensive foundation for constructing the OOD dataset but also covers a diversity of text styles and formats (further details can be found in the paper \"TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension\").\n\nSecondly, when selecting the triviaQA dataset as our foundation, we also considered that this dataset was published prior to the proliferation of AI text-generation technologies, reducing the likelihood of its being influenced by such texts.\n\n\nAbout Reproducibility:\n\nWe have noticed that the reproducibility score for our study is relatively low,  which raises significant concerns for us. To ensure the reproducibility of our research, we have taken the following steps:\n\n- We have provided the complete datasets in the supplementary materials.\n\n- The full code for SeqXGPT and all baseline methods used for data preprocessing, model training, and result testing is included in the supplementary materials. Necessary comments have been added to the code to facilitate understanding for other researchers.\n\n- A complete description of the environment settings is available in the supplementary materials, including version information for all dependency libraries, to ensure that other researchers can replicate our experimental environment precisely.\n\nConsidering the above, we believe that our research offers strong reproducibility. If you encounter any issues while attempting to replicate our research, we are more than willing to offer further support and clarification.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper will provide a practical guide to construct an AI-generated text detection software. In particular, this method can be generally applied to any collection of LMs (already or to-be available), which makes the method presented in this paper a nice addition to the practical armory of any NLP researcher or industrial programmer.", "reasons_to_reject": "The dataset used to train and test the proposed method appears to be somewhat narrow in how it is produced. As bootstrapping is the first-step for any new task, so OOD test results the authors provided are a must-and-nice addition, but the intensity of the OOD test should have been greater in my opinion (as, once the paper published, interested people will be doing their own OOD tests by implementing their own versions).", "questions_to_authors": "A. Would there be any other ways to create the dataset, other than seeding the human-generated sentence in the beginning and then using different LMs to fill up the rest?  My worries are related to possible systematic bias of the dataset due to how the dataset is created --- monotonically increasing tendency to be more likely to be AI-generated as more sentences appear? Would you consider your proposed SeqXGPT-Bench is guarded against this kind of bias? \n\nB. From a similar vein, how wide or extensive does the currently evaluated OOD dataset cover the possibilities of AI-generated texts? This may be topic-wise or format-wise. I raise this point from the perspective of a potentially interested human being to implement this type of detector for a similar use case, and I would be greatly appreciative if this approach would work like a charm in my use case.", "paper_topic_and_main_contributions": "This paper addreses sentence-level AI-generated text detection task. The proposed solution for this task is to utilize a collection of (L)LMs to produce a collection of per-token likelihood, and use CNN-transformer-FCN structure to determine per-token label whether the token is AI-generated or not. The paper also provides a reasonable set of empirical evidences of the proposed model being effective in solving the addressed task.", "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": "- line 360: wihte -> white\n- line 869: trivaQA -> triviaQA"}, "extra_text": {}}, {"review_id": "1T77OByRWS", "reviewer": "Reviewer_nDFk", "summary": "", "questions": "Question A: For each model, the author sets the maximum sequence length given the maximum GPU allowance. What is the maximum sequence length for each model in the experiment? Will the difference in maximum sequence length have a significant impact on the performance of each model? Why not set the same maximum sequence length for each model?\n", "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 sincerely appreciate your valuable comments!\n\n\n\nAbout Weakness #1:\n\nWe have added two more sets of experiments for the Particular-Model Binary AIGT Detection task, and the results are as follows:\n\n**GPT-J:**\n\n|              |  P.(AI)  |  R.(AI)  |  P.(H.)  |  R.(H.)  | Macro-F1 |\n| :----------: | :------: | :------: | :------: | :------: | :------: |\n|  log *p(x)*  |   80.2   |   73.2   |   30.5   |   39.4   |   55.5   |\n|  DetectGPT   |   81.0   |   56.8   |   27.7   |   55.5   |   51.9   |\n| Sent-RoBERTa |   89.7   |   96.7   |   84.7   |   62.3   |   82.4   |\n|   SeqXGPT    | **98.0** | **97.8** | **92.6** | **93.3** | **95.4** |\n\n**LLaMA:**\n\n|              |  P.(AI)  |  R.(AI)  |  P.(H.)  |  R.(H.)  | Macro-F1 |\n| :----------: | :------: | :------: | :------: | :------: | :------: |\n|  log *p(x)*  |   70.2   |   68.1   |   26.1   |   28.1   |   48.1   |\n|  DetectGPT   |   84.1   |   38.5   |   33.7   |   81.2   |   50.2   |\n| Sent-RoBERTa |   86.8   |   92.7   |   77.0   |   63.5   |   79.6   |\n|   SeqXGPT    | **97.3** | **94.7** | **87.0** | **93.1** | **92.9** |\n\nIt can be seen that SeqXGPT consistently outperforms other baselines, further validating the analysis in Section 5.2.\n\n\n\nAbout Question A:\n\nThank you for your insightful question regarding the maximum sequence length, as mentioned in line 430 of our paper.\n\nTo clarify, the maximum sequence length is consistently set to 1024 across all models involved in our experiments. This is the maximum context length that all pre-trained models we used can support. Therefore, the consistent sequence length of 1024 across all models addresses the concerns you raised.\n\nThe statement in line 430 was intended to emphasize that our inference servers are engineered not only to maintain this uniform maximum sequence length but also to perform parallel calculations to maximize the utility of the available GPU memory, thereby enhancing inference speed.\n\nWe appreciate your pointing out the potential for confusion in the original text. We will modify this sentence for clarity in the final version of the paper.\n\nAdditionally, the complete code for the feature extraction of the inference server is provided in the supplementary materials, where you can refer to the file `backend_utils.py` to confirm that the maximum sequence length is indeed consistently set to 1024 for all models.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1) The method proposed in this paper can effectively solve the difficulties of sentence-level AIGT detection.\n2) The experiments designed in this paper cover 3 different sentence-level AIGT detection settings, all of which achieve SOTA.\n3) This article is written smoothly and helps readers understand.\n", "reasons_to_reject": "1. In the setting of Particular-Model Binary AIGT Detection, the table of experimental results only includes the results of GPT-2 and GPT-Neo, lacking test results on other LLMs.\n", "questions_to_authors": "Question A: For each model, the author sets the maximum sequence length given the maximum GPU allowance. What is the maximum sequence length for each model in the experiment? Will the difference in maximum sequence length have a significant impact on the performance of each model? Why not set the same maximum sequence length for each model?\n", "paper_topic_and_main_contributions": "Different from the document-level Artificial Intelligence Generated Text (AIGT) detection considered by current workers, this paper introduces the sentence-level AIGT detection challenge and proposes a method SeqXGPT based on convolutional neural networks and self-attention networks by utilizing log probability lists of white-box Large Language Models (LLMs). This paper also constructs a sentence-level AIGT detection dataset. The experimental results show that the proposed method achieves SOTA in sentence and document-levels detection.", "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": "RgLkzPfsqW", "reviewer": "Reviewer_PKrT", "summary": "", "questions": "Please address the concerns in \"reasons to reject\"", "limitations": "", "rating": 4, "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 sincerely appreciate your valuable comments!\n\n\n\nAbout Question #1:\n\nWe fully understand your concern about the novelty of SeqXGPT, and we appreciate the opportunity to clarify the unique contributions and advancements that set our work apart from existing methods like DetectGPT and Sniffer.\n\n1. Fine-grained AIGT Detection: Unlike DetectGPT and Sniffer, which primarily focus on document-level AIGT detection, SeqXGPT excels in identifying AI-generated text at the sentence level. This is a paradigmatic shift in tackling the problem and represents a substantial innovation rather than a mere incremental improvement.\n2. Feature Selection: SeqXGPT utilizes perplexity lists to capture sequence characteristics of the text comprehensively. This is an innovative approach designed specifically for AIGT detection.\n3. Model Architecture: Our method integrates a completely new model structure inspired by speech processing techniques like wave2Vec, further setting it apart from existing methods.\n4. Performance: Our empirical tests demonstrate that SeqXGPT significantly outperforms DetectGPT and Sniffer in both sentence-level and document-level AIGT detection. It also notably outperforms Roberta-based methods.\n\nWe hope these points elucidate the novelty and advances that SeqXGPT brings to the field. We are open to further discussions and happy to provide additional information if needed.\n\n\n\nAbout Question #2:\n\nRegarding the comparison with state-of-the-art (SOTA) methods: Given that the field of AIGT detection is relatively new, there is currently no universally acknowledged SOTA model. Nevertheless, we have made every effort to include a variety of existing methods in our experiments. We've conducted detailed evaluations across multiple facets, including sentence-level, document-level, and OOD scenarios, to ensure our experimental design is as comprehensive as possible.\n\nOn the subject of statistical significance tests: While we have not conducted formal statistical significance tests, our method significantly outperforms existing models across various scenarios, which is sufficient to demonstrate the huge performance improvement of SeqXGPT. In the revised paper, we will also include these tests to scientifically validate the performance gains.\n\nAbout the evaluation datasets: We indeed acknowledge that real-world data would offer more robust validation. Our current focus on synthetic datasets is mainly due to the challenges associated with collecting real-world data. Despite this, we have carefully constructed an OOD dataset to closely reflect real-world scenarios and provide more convincing experimental validation.\n\n\n\nAbout Question #3:\n\nThank you very much for your careful review!\n\nOn the topic of \"incorporating semantic features\", we indeed acknowledge this concern and have addressed it in the Limitation section. In line 497, our analysis of the results from the Mix-Model Multiclass AIGT Detection experiment indicates that sentences generated by more advanced models tend to contain richer semantic features. Since SeqXGPT uses a collection of per-token likelihood as features, we think that incorporating semantic features could potentially assist our model further.\n\nWith regards to your comments on \"diverse instructions,\" we've addressed this in Appendix C and also on line 615 of the Limitation section. In this work, our primary focus is on sentence-level AIGT detection, rather than further analyzing the working mechanisms and better usage of instructions. This focus is a methodological choice designed to ensure that our efforts are devoted to the exploration of this novel challenge.\n\nYour point about \"more complex scenarios where a document contains sentences\" is well taken.Compared to previous works like DetectGPT, we have mixed human-created sentences and AI-generated sentences within the same document. Through experiments, we have proven that SeqXGPT can effectively address fine-grained AIGT detection. Based on such pioneer exploration, mixing sentences from multiple sources is merely incremental work based on the current experiment.\n\n\n\nAbout Reproducibility:\n\nWe have noticed that the reproducibility score for our study is relatively low,  which raises significant concerns for us. To ensure the reproducibility of our research, we have taken the following steps:\n\n- We have provided the complete dataset in the supplementary materials.\n- The full code for SeqXGPT and all baseline methods used for data preprocessing, model training, and result testing is included in the supplementary materials. Necessary comments have been added to the code to facilitate understanding for other researchers.\n- A complete description of the environment settings is available in the supplementary materials, including version information for all dependency libraries, to ensure that other researchers can replicate our experimental environment precisely.\n\nConsidering the above, we believe that our research offers strong reproducibility. If you encounter any issues while attempting to replicate our research, we are more than willing to offer further support and clarification.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Novel Contribution: The paper addresses the important and challenging task of fine-grained AI-generated text (AIGT) detection at the sentence level, which is a significant advancement over existing document-level AIGT detection methods. The authors propose a new approach called SeqXGPT, which demonstrates promising results in both sentence and document-level AIGT detection challenges. This novel contribution fills a gap in the literature and provides valuable insights into fine-grained AIGT detection.\n\n2. Performance and Generalization: The experimental results show that SeqXGPT outperforms existing methods, such as DetectGPT and Sniffer, in sentence-level AIGT detection. SeqXGPT exhibits excellent performance not only in discriminating human-generated sentences but also in detecting AI-generated sentences. Furthermore, SeqXGPT demonstrates strong generalization capabilities on out-of-distribution datasets, indicating its robustness and potential for real-world applications.\n\n3. Dataset Construction: The authors synthesize a sentence-level AIGT detection dataset, which is crucial for studying fine-grained AIGT detection", "reasons_to_reject": "1. Lack of Novelty: The paper does not present a significant advancement or novel contribution to the field of fine-grained AI-generated text (AIGT) detection. The proposed approach, SeqXGPT, is similar to existing methods such as DetectGPT and Sniffer. The paper fails to demonstrate how SeqXGPT significantly outperforms or improves upon these existing methods.\n\n2. Insufficient Experimental Evaluation: The experimental results provided in the paper are limited and do not provide a comprehensive evaluation of the proposed approach. The paper lacks a thorough comparison with state-of-the-art methods and fails to provide statistical significance tests to support the claimed performance improvements. Additionally, the evaluation is primarily focused on synthetic datasets, which may not accurately reflect real-world scenarios.\n\n3. Incomplete Analysis and Discussion: The paper lacks a thorough analysis and discussion of the limitations and potential drawbacks of the proposed approach. For example, the authors do not explore the impact of incorporating semantic features or investigate the influence of diversified instructions on AIGT detection. The paper also does not address more complex scenarios where a document contains sentences.\n\n", "questions_to_authors": "Please address the concerns in \"reasons to reject\"", "paper_topic_and_main_contributions": "This paper introduces a method called SeqXGPT for detecting AI-generated text (AIGT) at the sentence level. The method utilizes log probability lists from white-box language models as features for detection. Experimental results show that SeqXGPT outperforms baseline methods in both sentence and document-level detection challenges and exhibits strong generalization capabilities.", "excitement": 4, "reproducibility": 1, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper introduce a sentence-level AI-generated text detection challenge by synthesizing a dataset that contains documents polished with LLM. It also proposes SeqXGPT, a method based on convolutional neural networks and self-attention networks by utilizing log probability lists of white-box Large Language Models (LLMs). The reviewers agree that the paper makes a novel contribution to the field, and the experiments clearly establish the effectiveness of the proposed approach. Reviewers also appreciated the additional results provided by the authors during rebuttal, which should be added in the main paper.
Decision: Accept-Main