ChatbotID: Identifying Chatbots with Granger Causality Test
Authors: Xiaoquan Yi
Haozhao Wang
Yining Qi
Wenchao Xu
Rui Zhang
Yuhua Li
Ruixuan Li
Venue: NeurIPS 2025 poster ()
Keywords: Granger Causality
Dialogue
LLMs
Primary area: applications
TL;DR: We develop reliable methods to accurately identify whether an interlocutor in real-time dialogue is human or chatbot

Abstract: With the increasing sophistication of Large Language Models (LLMs), it is crucial to develop reliable methods to accurately identify whether an interlocutor in real-time dialogue is human or chatbot. However, existing detection methods are primarily designed for analyzing full documents, not the unique dynamics and characteristics of dialogue. These approaches frequently overlook the nuances of interaction that are essential in conversational contexts. This work identifies two key patterns in dialogues: (1) Human-Human (H-H) interactions exhibit significant bidirectional sentiment influence, while (2) Human-Chatbot (H-C) interactions display a clear asymmetric pattern. We propose an innovative approach named ChatbotID, which
applies the Granger Causality Test (GCT) to extract a novel set of interactional features that capture the evolving, predictive relationships between conversational attributes. By synergistically fusing these GCT-based interactional features with contextual embeddings, and optimizing the model through a meticulous loss function. Experimental results across multiple datasets and detection models demonstrate the effectiveness of our framework, with significant improvements in accuracy for distinguishing between H-H and H-C dialogues.

ChatbotID: Identifying Chatbots with Granger Causality Test
Xiaoquan Yi', Haozhao Wang',, : Yining Qi' Wenchao Xu²,
Rui Zhang's Yuhua Li', Ruixuan Li
School of Computer Science and Technology,
Huazhong University of Science and Technology. Wuhan, China
School of Division of Integrative Systems and Design
Hong Kong University of Science and Technology, Hong Kong, China.
tyixiaoquan, ha_ hz_ wang) thust. edu.cn. cn, rayteaméyeah.net
Abstract
With the increasing sophistication of Large Language Models (LLMs), is crucial develop reliable methods to accurately identify whether interlocutor in real- time dialogue is human or chatbot. However, existing detection methods are primarily designed for analyzing full documents, the unique dynamics and characteristics of dialogue. These approaches frequently overlook the nuances interaction that essential in conversational contexts. This work identifies key patterns in dialogues: (1) Human-Human (H-H) interactions exhibit significant bidirectional sentiment influence, while (2) Human-Chatbod (H-C) interactions display clear asymmetric pattern. We propose an innovative approach named ChatbotID, which applies the Granger Causality Test (GCT) to extract a novel set of interactional features that capture the evolving, predictive relationships between conversational attributes. By synergistically fusing these GCT-based interactional features with contextual embeddings and optimizing the model via a structured loss function, we significantly enhance the model's ability to capture asymmetric influence in H-C dialogues. Experimental results across multiple datasets and detection models demonstrate the effectiveness our framework, with 15.92% improvements in accuracy for distinguishing between H-H and H-C dialogues.
Introduction
The rapid udvancement and proliferation of Large Language Models (LLMs) have led to increasingly sophisticated conversational agents capable of generating remarkably human-like text 2 3 By exploiting the sophisticated conversational abilities of LLMs, malicious actors can convincingly simulate human interactions [4, 5 6 tricking unsuspecting individuals into believing they communicating with a real person, thereby facilitating fraudulent activities such as scams and identity theft 7 g Considering this, has become critically important devise reliable methods for distinguishing between human and LLM-driven interactions.
Many -fate-of-the-ar methods typically rely on identifying statistical anomalies in linguistic 9 10 I1] or stylometric features 12 [3] for detecting LLMs- text. These approaches often require extensive manual feature engineering and may exhibit limited effectiveness against more advanced LLMMs that are explicitly optimized to bypass such detection mechanisms. More recently, supervised learning approaches [14 15 16] have been developed distinguish between human- written and LLM -ggnerrtd text by analyzing both semantic content
[7 [8 and high-level textual
"Haozhoo Wang is conresponding authors.
Homepage: https:://// h/www/wwwww/cccoo wwwzhang...n....m info/
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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features [9 20 21. Although these models are effective in certain contexts, they primarily analyze the static textual content and stylistic features of full documents. Consequently, they often overlook the fine-grained interactional nuances necessary for dialogue detection. These limitations underscore the urgent demand for innovative dialogue detection approaches capable of capturing fine-graines interaction dynamics and integrating them with semantic representations to enable more reliable identification of conversational participants.
In this work, we are developing a specialized detection framework to identify chatbot text in dialogues, focusing on unique linguistic features and interaction patterns. Particularly, we reveal two principal patterns of sentiment influence within dialogues: fliman-human (H-H) interactions are character- ized by substantial bidirectional sentiment exchange, whereas Human- Human-Chatbot (H-C) interactions demonstrate distinct asymmetric inftuence. Motivated by two principal patterns, we propose a novel approach named ChatbotID that integrates the deep contextual understanding capabilities of L.MSs with quantitative analysis of conversational interaction dynamics derived from Granger Causality tests [22 (GCT). To quantify these temporal dependencies, we first extract relevant time series features (e.g., sentiment scores per turn) from the dialogues. Subsequently, we apply GCT to these features to compute a feature vector, denoted as VGCT. This vector is utilized to fine-tune LLMs specifically for the task of distinguishing dialogues generated by LLMs. By jointly modeling semantic content and interaction dynamics, the model becomes proficient at identifying chatbots. Experiments show our framework significantly improves accuracy in distinguishing H-H from H-C dialogues across multiple datasets. The main contributions of this work are:
Grounded in Communication Accommodation Theory, this work is the first to systemati- cally quantify and reveal two principal patterns of sentiment influence in dialogues: H-H interactions are characterized by statistically significant bidirectional influence, whereas H-C interactions demonstrate a distinct asymmetric inftuence pattern.
We propose a novel dialogue detection method based on GCT named ChatbotID. ID. To our knowledge, this work is the first to systematically uddress the detection of LLM -enereeee contributions specifically within conversational contexts.
Extensive experiments conducted on various datasets (DailyDialog, MultiWOZ, etc.) and advanced LLMs (Gemma, Qwen-2, Deepseek-Re etc.). demonstrate that our method outperforms state-of-the-art detection methods by up to 15.92%
2
Related Work
In this section, we discuss three critical dimensions of LLMs-generated text analysis, i.e., representa- tive detection approaches, the Granger Causality Test 22 and Interaction Dynamics.
LLM--Genratte Text Detection. Various approaches achieve differentiation between human and LLM--enerated texts by capitalizing on the complex inner workings of LLMs [[2 I 23).
specifically examining aspects like intermediate layer outputs and model weights (24]. However, these methods reliant on internal model information also encounter notable limitations, such as their inapplicability to black-box proprietary models, and weaker generalization across diverse model architectures
25 26 Another category of detection approach shifts focus to the statistical properties
of the text itself 9 27 28 29). They utilize statistical metrics (e.g. entropy, perplexity, frequency of specific words, sentence structure) to differentiate between LLMs MeMM--ggeertted and human- comman-written However, these detection performances can also be significantly affected by variations in text type, topic diversity, and specific linguistic characteristics, leading to insufficient stability and accuracy. Other researchers have adopted supervised learning methods [4] 155 [6], training specialized classification models on large datasets of labeled texts. While these models demonstrate effectiveness in certain contexts, their primary analytical focus is the static textual content and stylistic features of entire documents. They often fail to capture the interactional nuances for dialogue detection.
texts [30 31 32
Granger Causality in Text Detection. GCT, an econometric concept by origin, is standard statistical method used to determine if one time series improves the forecast of another [2 33 34.. GCT provides a valuable statistical framework for investigating directional predictive relationships between time-ordered data sequences, finding pertinent applications in detection tasks within Natural
Language Processing 35 36 37 38) Existing methodologies for GCT are centered on its core
concept of evaluating whether one-time series" past significantly improves the prediction of another's
2

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beyond the information contained in the target series' history. These methods provide a comprehensive toolkit for identifying and characterizing directional predictive links in temporal data across various domains 42
future [39 40
Linguistic Accommodation and Interaction Dynamics. A foundational concept for understanding dialogue is Communication Accommodation Theory, which posits that individuals adjust their communication strategies signal social closeness, gain approval, or maintain social distance [45 46). This theory has motivated a significant body of work studying linguistic accommodation, where conversational partners tend to converge in their use of linguistic features, such as style, syntax, and sentiment [47 48). Many research have successfully leveraged metrics of accommodation to et
analyze social dynamics in various contexts [49 [50] For instance,
al. [S]] demonstrated that power imbalances in conversations are reflected in asymmetric linguistic coordination patterns. Studies on online discussions have shown that interaction dynamies, including accommodation, are predictive of persuasion and argument outcomes, and can help in detecting disputes [52]. These works typically quantify accommodation using correlation- based metrics or measures of distributional similarity between speakers' features over conversation.
3 Motivation: Asymmetric Influence in H-C Dialogues
By comparing H-H dialogues with H-C dia- logues, we observe that chatbots exert asym- metric conversational influence. Human con- versation is not merely sequence of contextu- ally relevant utterances [3] 54]. It is rich, dynamic process characterized by mutual influ- ence, adaptation, and intricate feedback loops
Sentiment Infiuence Dynamics
p- D.05 Unresnals
Usert -s User2 praatue
aoo-oa sed Causatty (1
User2 User (GC1 p-vatos)
a.a-
operating over time [55 49 This reciprocity shapes phenomena such as sentiment contagion, topic negotiation, and behavioral entrainment, reflecting an underlying dynamic coupling be- tween participants 45 50). While an LLM might react with high sensitivity and predictabil- ity to user input (e.g., user sentiment strongly and immediately driving LLM sentiment), it may exert significantly less reciprocal influence in dynamically shaping the user's subsequent state or cognitive framing compared to a human
Iturnnnd (11-1)
Human-LLM
Daligue Type
Figure The figure demonstrates that H-H di- alogues show significant bidirectional sentiment influences, whereas H-C dialogues feature a pro- nounced asymmetric pattern.
partner [47 56 [2
GCT offers powerful statistical framework for examining the predictive relationships between time series derived from dialogue features (e.g.. sentiment scores and utterance lengths). allows us to test whether the history of one participant's conversational features (e.g.. the User's sentiment time series) significantly improves the prediction of the other participant's future features. As shown Figure [ ment dynamics within 200 dialogues from the DailyDialog dataset. In H-H interactions, there is statistically significant mutual influence. Userl significantly affects User2 (mean p-value 0.04), and User2 reciprocates with stronger influence on Userl (mean p-value = 0.01). In contrast, H-C dialogues exhibit a clear asymmetric pattern. GCT p-values indicate that only the human user exerts a statistically significant influence the LLM's sentiment (mean p-value 0.03), whereas the LLM's sentiment does not significantly influence the user (mean p-value- = 0.45). These distinct sentiment causality structures highlight the contrast between statistically significant bidirectional influence in H-H dialogues and the unidirectional, user-driven dominance observed in H-C interactions.
we apply GCT to examine senti-
4 Methodology
In this section, we detail a method named ChatbotID for human versus LLM dialogue classification by fine-tuning an LLM using Multi-Task Learning. Our approach combines the LLM's semantic rep- resentations with GCT metrics derived from interaction dynamics. ChatbotID leverages interaction dynamics and semantic-fouusee attribution to enhance classification accuracy.
3

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Dillogac
Semantic
effective sample size of Hey, Sanitl Long time no aec. How have you bece!?
S2
Semantic Predictor
"HIII Dialogues"
well, stated
00
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9%
"H.- Dinlogues"
Wew thet onsed orroiing" Italy beautifud: for alwaye wanted Hove fastastic sig!
LIM
Predictor
Letess
F
Defishely Just let me know when there unydting the
CC Exstrcet
G
?
GCT Interaction Predictor
help you
G
"H-I Dialogues"
Gg
Bistnngish
GCT Vector
GCT Interaction Lovs
Figure 2: ChatbotID is purpose-built for the detection and analysis of dialogue scenarios. The Semantic Loss L, associated with the Semantic Predictor focuses on capturing the deep semantic understanding of dialogues. The Detection Loss (Letass) drives the model to perform classification tasks, categorizing dialogues into predefined classes. The GCT Interaction Loss (Tag) ensures the model learns and leverages the interactional features and patterns extracted by the GCT module.
4.1 Problem Formulation
Let D {(C(,, ) be a dataset comprising N dialogues. Each dialogue C() consists of sequence of user (U) and agent (A) utterances, paired with a label y E {0. denoting the agent's type (0: Human, 1: Chatbot). The problem is learn parameterized function fo C - (0, 1). where 0 represents the model parameters. This function aims to estimate the posterior probability Ply 1(C) for any given dialogue C. The parameter 0 is optimized minimize loss function reflecting the classification error on the dataset D.
4.2 Feature Engineering
Dialogue Time Series Extraction. For each dialogue ( comprising T; interaction points (e.g., turns, utterances per participant), we extract features for both the user and agent at each point "il...,T]
t e [1,..., T.}. The pre-defined feature set Q includes basic metrics (e.g.. utterance length, topic embedding components) along with semantic features(c.g, sentiment scores, topic embedding components), extracted using LLMss Let the feature extraction function be E. This yields paired numerical time series:
E: C00 + (IXP L
(I)
feQ
where x1f. IR and x R the values for feature time t.
Granger Causality Feature Vector Calculation. The GCT is employed to assess predictive causality between selected pairs user and agent feature time series. We perform a specific test determine whether time series X, Granger-caue cauges time series Y,, employing lag order of p. X, and Y, represent specific feature sequences, e.g. (ff))
x x(fi(() (t) for X., and x((a) (t) x x(22)()) (t) for Y,. To conduct this test, two linear autoregressive models are estimated using Ordinary Least Squares over the effective sample period p 1, 1,... T.. This period corresponds T.
The baseline is the restricted model, where Y, is modeled solely based on its own past values:
p
Y, = ag
ag Y CR.b
k=

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The fit of this model is measured by its Sum of Squared Residuals, SSR = Σ &. This is contrasted with the unrestricted model, which incorporates lagged values of X₁ as potential predictors for Yg:
Y2 30 + B k + E Nr + EURA
URR A The corresponding Sum of Squared Residuals for this model is SSRUR = E statistically significant reduction from R to SSRER indicates that X, Granger-causes Y, Such predictive relationships within the dialogue's 's interaction dynamics, captured by comparing these models, offer valuable signals for distinguishing between human and chatbot.
The null hypothesis Ho 71 72
%p 0 posits that X does not Granger-caus, Y. KUR = 2p + is the number parameters the unrestricted model. This hypothesis tested using the F-statistic:
(SSRR SSRrR\/D (SSRR SSRUR)/P (SSRon)/(n kvR) (SSRun)/ 2p 1)
Under Ho. the statistic follows an F-distribution, F ~ F(p, n - 2p- - 1). The p-value is computed as:
p-value = P(Fp.n-2p 2p-1 2 F(H))
We specify the pairs of conversational features to compare (e.g.. sentiment agent reply length), the direction of potential causality being tested (User-to-Agent or Agent-to- toe-Ur and the relevant time lags p. Subsequently, for each dialogue (+)) in our dataset, this entire suite of pre-defined tests is performed; let dGCT be the total number of such tests. Each test k (k 1,... docr) yields (2) for dialogue (()), typically a p-value reflecting the significance of that specific predictive relationship. Finally, all these daer outcomes about dialogue (1i) are gathered and concatenated into single numerical list, forming the dialogue's GCT feature vector:
a statistical outcome 9k.
GCT = [9] .9 -dccrr
(6)
4.3 Classification Loss
The classification loss is employed to enable the model distinguish between human users and LLMs within conversational environment. For given dialogue input p, associated with its true class label y and predicted probability distribution ÿ. K 2, corresponds the two possible sources of the dialogue: human and LLLMs-generated, and the cross-entropy loss is formulated as:
Lelass
Uk log 0k
1=1
4.4 Sesmantic-Focuseee Attribution Supervision
To enhance the model's understanding of semantic differences between H-H and H-C dialogues, particularly the quality of L.LMs contributions in H-C contexts, introduce supervision mechanism based on semantic-focusee attributions. These attributions are identified by querying an LLM to detect specific undesirable characteristics failures within given dialogue. The LLM is prompted using the following method to generate these semantic attributions:
Let C {Cgoal Clact Ceommon: Clogic be the predefined set of pragmatic deficiencies. For each dialogue D, our training set, the LLM's output parsed to generate binary deficiency attribution vector ay gaj, Cjjfacts commmnn do..og Each element a,,x (where corresponds to deficiency in C) is defined as:
1,
if dialogue D, is identified exhibiting deficiency Ck:
0j.k 0, otherwise.
(8)
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Input Dialogue:
[Dialogue Text]
Contextual Focus (if identifiable as potential H-C): Contributions from the suspected chatbot.
Question: Which of the following pragmatic semantic deficiencies does this dialogue exhibit, particularly concerning the contextual focus if applicable?
1. Goal Obfuscation/Failurs (Cgoal): The primary user's goals seem unmet, poorly addressed, significantly side-tracked.
2. Factual Inconsistency (Crae)::
The dialogue contains statements that are demonstra- bly false, misleading, or internally inconsistent with established facts.
3. Commonsense Violation (Ccommon): The dialogue includes statements, reasoning, assumptions that clearly contradict basic, everyday commonsense.
4. Logical Incoherence (Cloiie:: The dialogue displays internal contradictions in rea- soning, significant logical fallacies, breakdown in coherent argumentation.
If multiple deficiencies are applicable, provide comma-separatee list of the corresponding labels (e.g., "Cgual, Coummon") "). Answer "None" if none of the options apply.
To guide the main classification model using these semantic deficiency attributions, we train to jointly predict these attributes. This is achieved by defining an auxiliary semantic deficiency Attribution loss (Ls). Assuming the model produces a corresponding vector of predicted probabilities å goal, @j. factt commmnn (oooic for each deficiency type for dialogue Dj, the loss is:
ND BCE(a az.k)
Ls = ND
j-1 KEC
where ND is the total number of dialogues in the training batch, k iterates over the set of deficiencies C, and BCE denotes the binary cross-entropy loss.
4.5 Causal Interaction Dynamics Supervision using GCT
In our motivation phase, we discover a pattern indicating that the presence absence of statistically sigmificant causal links, as reflected by the corresponding p-values, serves as a distinguishing feature between H-H and H-C interactions. To leverage this, we transform the GCT p-values into binary indicators of significant causal effects. Let Osii be a pre-defined significance level (e.g., 0.05). For each dialogue (+) and each GCT test outcome g
we define a binary causality indicator b(0).
-f11 1, if 9 < Ossig (indicating significant causal link), $10, otherwise (no significant causal link detected).
(10)
This process yields a binary GCT vector ( [b
for each dialogue C(O.
doer
Our main model is then tasked with jointly predicting these binary significance indicators. This is
achieved by incorporating an auxiliary GCT Significance Loss (Lg). Assuming the model produces
corresponding vector of predicted probabilities ((0) = loss is defined as:
[B(
(1) ((0) g(t)
..... door for dialogue C()), the
No dger
Σ Σ BCE( ( ((0)
LG
(11)
ND
where deer is the number of GCT tests performed, and BCE is the binary cross-entropy loss.
4.6 Final Objective Funetion
This LG is added the overall loss function, alongside the primary classification loss Letass and semantic-focused attribution Ls:
L Lelass + Ls + LG
(12)

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5
Experiments
In this section, we present the experimental setup, detailing the datasets used and the implementation of our methods. We evaluate the performance across multiple datasets using metrics (e.g., accuracy. F1-score) with ofate-of-the-ar methods.
5.1 Experimental Setup
Datasets:
To evaluate our proposed methodology across different conversational settings, we utilize four prominent English-language dialogue datasets: two focused on open-domain chit-chat (c.g.. DailyDialog [57], PersonaChat (58) and two on task-oriented interactions (c.g., MultiWOZ [59],
Taskmaster-1 60 These datasets serve as the foundation for constructing both our H-H and H-C dialogue corpora, ensuring comparability in style and domain. The H-H corpus used in our experiments is formed by selecting dialogues directly from the aforementioned datasets. Dialogues below a pre-defined length are filtered out to ensure suitability for Granger Causality analysis. The H-C corpus a semi-synthetic dataset derived from the H-H corpus to ensure high comparability in user input and conversational context. The construction invol selecting H-H dialogues, each comprising user turns and original human agent turns. For every dialogue, we identify the human agent's utterances and then prompt an LLM (e.g., Llama-2-Chat 70B GPT-4) to generate new responses for those specific turns.
Metrics. To rigorously evaluate our method's 's ability to distinguish between H-H and H-C dialogues. We employ three primary performance metrics on a held-out test set: Accuracy (ACC), Area Under the Receiver Operating Characteristic Curve (AUROC), and the F1-score (F1).
Baselines. We conduct rigorous comparative analysis of our proposed methodology against several state-of-the-art methods for detecting I.M--geerated text. DetectGPT 12) identifies synthetic text by scrutinizing the local curvature of source language model's probability function around given text passage. Fast-DetectGPT [3] detects LLMs-generated text by evaluating the conditional probability curvatures of sampled token alternatives. T5- T5-Sentinel [61] introduces a supervised learning approach that reframes LLM -LM-genertted text detection as a token prediction task, using labeled data to fine-tune T5 models to directly predict text sources. COCO [22 employs contrastive learning to enhance detection by learning discriminative representations that separate -eee-eeed from human- authored texts in the embedding space. RoBERTa-MPU specifically for LLM -LM-eeeerated text detection. OUTFOX
63 is a standard RoBERTa model fine-tuned 7 enhances the robustness of detecting MMMsggenertted texts by implementing iterative in-context learning between the detector and an attacker that generates adversarial examples. I.LMDet [64] employs surrogate perplexity calculations specifically tailored to individual I.LMs. Shifting to a structural representation. SeqXGPT (TT] transforms sentences into waveforms, utilizing convolutional networks and celf-aattntion mechanisms for detection at the sentence level. GECScore [65] provides a robust metric for discerning LLM origins by evaluating text similarity through the lens of a grammar error correction model.
General-purpose LLMs. In our study, we employ a selection of representative general-purpose LLMs as analytical benchmarks, leveraging their inherent capability for zero-shot veracity prediction. This approach facilitates the direct assessment of truthfulness without necessitating specialized
1.55
0/48
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1.90
67M
0316
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outrts
caco
hatlo?
Figure 3: The visual data presented in the graphs clearly indicates that our methodology excels in detection accuracy and Fl score on the Taskmaster-1 I dataset.
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fine-tuning procedures. The LLMs selected for analysis include LLaMA-7B [66]. LLaMA-13B, GPT-3,5-turbo, GPT-4, Gemma [67]. Qwen-2 [68]. and Deepseek-R2 69 These models are utilized as standards to systematically evaluate both the capabilities and limitations of LLMS in performing zero-shot detection of content-generated tasks.
Implementation Details. We implement our proposed methodology using the Hugging Face Trans- formers library. The model is fine-tuned on the constructed H-H and H-M datasets, with batch size of 16 and a learning rate of 2e-5. The GCT analysis is performed using the statsmodels library, with maximum lag of 5 for Granger causality tests. The model is trained for 20 epochs, with early stopping based on validation loss. The model is evaluated on separate test set, and the results were averaged over runs to account for variability in training. The model is trained utilize the Adam optimizer, incorporating weight decay to enhance regularization (70 71 72..
5.2 Performance evaluation
Table 1:
Experimental results on the DailyDialog, PersonaChat, and MultiWOZ datasets. The best number is highlighted in bold, while the second best one is underlined. Our approach consistently outperforms other methods, achieving the highest accuracy in each dataset.
DailyDialog
Persona Chat
MultiWOZ
Method
ACC
F1
ACC
F1
ACC
F1
DetectGPT (ICML 2023)
60.36 1.37
67 0
65,42
2:20
65.24
1.96
64.59
5.19
62.36
1.23
COCO (EMNLP 2023)
77.36 201
1.3
78.56
1.74
77.64
2213
78.86
1085
75.41
+3.54
LLMDet (EMNLP 2023)
64.77 225
70.28
67.22
0.56
66.00
=0
67.24 1.58
67.27
1.97
SeqXGPT (EMNLP 2(123)
65.63 2.57
66.82
69.54
1.30
65.02
2.06
67.14
1.56
67.05 +0.19
Fast-DetectGPT (ICLR 2024)
62.71 2.65
62.28
1:32
63,43 3.02
64.17
1.71
59.86 0.37
62.46
1.79
TS- TS-Sentinel (EMNLP 2024)
76.68 2.39
74.63
3.15
72.84 2.05
73.77
2.61
84.52 2.11
77.47 1.08
RoBERTa-MPP (ACL 2024)
78.62 0.38
81.15
0.96
2.75 82.05
83.35
1.40
83.24 1.53
82.04 0.24
DeTeCtive (NeurIPS 2024)
72.31 0.53
74.83 0.356
75.95
73.99
1.25
80. 1:27
77.74
OUTFOX (AAAI 2024)
78.80 090
83,46 1.13
80.06 0.31
84.08
82.77 2004
81.11 1.22
GECScore (ACL. 2025)
69.05
72.81 1.06
75.36 4.12
73.60
75.54 3:32
67.55 0.16
GPT-3.5-turbo (2023)
51.42
59.11
61.00 1.07
66.84
60.37 #0.54
$9.62
LLaMA-7B (2024)
58.17
60.52
61.61
18.9
65.47
59.54 +2.32
LLaMA-13B (2024)
60.94
62.81 2.27
63.32 0.26
63.53
65.16 0.30
62.35 1.13
GPT-4 (2024)
62.61 3393
64.56
65.28 0.87
62.09
62.74 +087
58.94 2
Gemma (2025)
63.67 2.76
65.19
66.31 1.86
68.35 0.48
66.99 4.72
64,91 1.40
Qwen-2 (2025)
61.92
66.35
1:0
65.50 0.71
64.95
20.49
65.43
63.97 4.66
Deepscek-R1 RI (2025)
65.68 0.20
67.96 1.47
66.27 2.60
62.25
68.03
69.44
ChatbotID (Ours)
82.77 20.5
84.74
82.23 1.29
87.01
2.92
87.38
1 4.188
84.38 10.9
Accuracy. As shown in Table [ ChatbotID model consistently achieves the highest accuracy across multiple diverse dialogue datasets. For instance, the MultiWOZ dataset, ChatbotID's 's accuracy reaches 87.38%, which is notably higher than other Icading specialized detectors such as RoBERTa-MPU (83. (3.24%) and T5-Sentinel (84.52%). In contrast to detection methods that primarily rely on static text features or stylistic analysis (e.g.. DetectGPT, COCO, LLMDet), ChatbotID gains its performance edge by analyzing the dynamic interactive features within dialogue, particularly by employing the GCT to capture predictive relationships in attributes like sentiment. The GCT provides statistically grounded way to quantify influence and predictive causality within dialogue. This is more targeted approach than relying solely on learned representations from LLMs, which might not inherently focus these subtle interactional cues crucial for distinguishing nuanced LLM behavior from human behavior.
F1-Score. As shown in Figure ChatbotID records an Fl-score of 0.828. This is considerably higher than the other methods, including OUTFOX (0.773), RoBERTa-MPU (0. (0.7,8), and COCO (0. (0.736). The general-purpose LLM, Gemma, shows a much lower F1-score of 0.620. Chatbot ID provides a statistically grounded way to quantify influence and predictive causality within a dialogue. This is more targeted approach than relying solely on learned representations from large pre- pre-trained models, which might not inherently focus on these subtle interactional cues crucial for distinguishing nuanced LLM behavior from human behavior.
AUROC. As shown Figure across four distinct dialogue datasets, ChatbotID consistently achieves the highest AUROC scores when compared against seven other text detection models.
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DailyDialgg
Multi WOZ
1.0
1.0
0.8
0..8
Trae Poekive Rate
0.6
0.6
Troe
DetaaGeT (AUROC STAT))
DMterGPP (AUROC 1150
Dacerri (AUROC- 62
Fas Desestof (AUROC-
V2-iemzed (AUROC- 64.7%9)
12-Senind (AUBOC- 13.229
CISCO (AUBOC-- TEURPS
cocoa (AUBBOC-
82.009)
RoltiiTa (XtTROC - 71.000
38%0
OUTIOO (AIIK00-
Our BOCC-
Boor 100g
Falso Posilive Rate
Falsu Posttive Rate
1.0
1.0
0.8
Thue Povitvve Raie
Thme Positive Rate:
0.6
DeseRPT LAUBOC- -66--66-11 252
DeseGPP (AUROC- T2A0%)
Fas BewcKFP LATROC = 1159)
Fan DINNGET DI CAUURO (15)
17 SeTTel(AAARRC (4700))
SArtal(AERROC 71.7%0
COCO (AUROC (05750)
02-
COCO (AUROC- (84450
RaBEK KIPU (AUBOO- 13,00))
RaBEKC MPP (AL (AU8OO--1U55 71,550
OUTPOX (AUBOC 79.12%
OUTFOX SAUROC 713156)
Our (AUROC" 96.055
(AUBOCC $5,55)
Figure 4: The figure displays ROC curves illustrating the comparative performance of seven different text detection models across various datasets.
Specifically, ChatbotID ID attains an AUROC 86. 10% on DailyDialog, a remarkable 90.16% on MultWWOZ, 86.69% PersonaChat, and 88.55% T This consistent outperformance across varies conversational contexts, from open-ended chit-chat to structured task completion, un- derscores a key advantage of our approach. Unlike methods that rely predominantly static textual features or stylistic anomalies, ChatbotID incorporates an analysis interaction dynamics. Em- ploying Granger Causality tests quantifies the predictive influence between conversational attributes of the user and the agent over time.
Table 2: On various LLM backbones, ChatbotID demonstrates consistent improvements in accuracy.
Method
DailyDialog
Persona
MultWWZ
Taskmaster-1
LLaMA-7B
61.74 1.38
59.05 +3.17
58.32 +3.34
55.44 1.65
ChatbotID-LLaMA-7D
70.26 0.64
72.87 2.62
72.47 1.28
66.48 1.46
Gemma
63.20 = 4.94
57.62 a 1.08
63.59 4.21
59.17 2.15
ChatbotID-Gemma
69.12 0.69
71.91 1.34
74.75 4.64
76.25 1.43
Qwen-2
60.80 2.11
63.59 0.66
61.70 0.01
63.04 2.68
SlatbotID-Qwen-2.
79.99 3.50
82.56 + 1.59
89.63 1
85.34 = 2.48
Deepseek-RI
60.37 2.16
65.29 1.24
61.65 2.48
63.50 2.21
ChatbotID-Deepseekk
82.07 0.66
84.35 + 1.25
85.77 # 1.58
84.15 1.28
Different LLMs backbones. Table systematically demonstrates that integrating the ChatbotID framework leads to substantial and consistent accuracy improvements when applied a variety of LLM backbones for the task of distinguishing human-LLM dialogues. For LLaMA-7B, the introduction of ChatbotID elevates accuracy from a baseline 61.74% 70.26% on DailyDialog, from 59.05% to 72.87% on PersonaChat. When applied to Qwen-2, ChatbotID shows particularly striking gains, boosting accuracy on Taskmaster- from 63.04% 85.34%. The magnitude of these improvements, often exceeding 10-20 percentage points (e.g., Qwen-2 MultiWOZ shows an

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increase of nearly 28 percentage points), highlights the significant value added by ChatbotID. This advantage stems from ChatbotID's 's use of GCT extract and integrate distinctive interactional dy- namics, combined with M.L.-b semantic understanding through a structured multi-task learning framework, enabling more nuanced detection.
Table 3: Ablation study: This table illustrates the individual and combined contributions of the ChatbotID's s distinct loss components to its overall accuracy in distinguishing H-C dialogues
Method
DailyDialog
PersonaChat
MultiWOZ
Taskmaster-1
Lclass Leloss + Lg
63.19 1.63
67.86 1.97
69.16 0.63
66.18 4.93
67.58 a 2.45
72.73 = 0.79
74.71 = 1.08
74.35 1.59
Lelase + LG
70.99 3.88
78.70 8.55
77.70 2.49
80.38 1.29
Ledaas + LG Lg
80.23 3.82
83.63 1.74
87.01 0.64
83.37 2,70
Ablation study. As shown in Table the baseline model, relying solely on the classification loss Leioss, establishes foundational level of performance across datasets. The introduction of the semantic-focused attribution supervision Ls yields consistent accuracy improvements (e.g., from 63.19% to 67.58% % on DailyDialog) demonstrating the value of guiding the model to recognize specific semantic deficiencies often present in chatbots. More profoundly, the integration of the causal interaction dynamics supervision using LG provides more substantial boost in accuracy (e.g.. from 69% to 70.99% on DailyDialog when combined with Letass The semantic deficiency attri- bution loss Ls helps the model identify common LLM pitfalls, further refining its classification and potentially reducing false negatives where an LLM produces superficially coherent but pragmatically flawed dialogue.
Table 4: Performance across dialogue turn ranges on DailyDialog, PersonaChat, and MultiWOZ.
Daily Dialog
PersonaChat
MultiWOZ
Turns
ACC
F1
ACC
F1
ACC
F1
1-5
60.07
+ 0.38
62.39
+ 0.98
61.29 + 1.97
61.86
0.51
60.89 0081
63.26 +
6-10
75.88 0.60
78.12 # 1.48
76.90 3.36
75.96 + 0.27
78.96 # 1.50
78.34 + 0.48
10-15
83.41
+: 0.66
82.83
: 193
84.96 + 0.54
82.44 + 0.83
84.86 : 3.08
83.72 2.53
15+
86.82 +: 85.49 + 1.45
86.95 + 1.24
83.91 + 1.95
89.17 + 2.75
84.77 + 0.29
Dialogue Turns. In the initial stages of the dialogues, from 1-5 turns up to 10-15 turns, the model exhibits dramatic and consistent improvement in both accuracy and F1-score across all three datasets. For instance, on the MultiWOZ dataset, accuracy skyrockets from 60.89% in the 1-5 turn bucket to 84.86% in the 10-15 turn bucket. very short dialogues, there is insufficient interaction history to establish a stable pattern of influence. As turns accumulate, the cause-and-efice chain between speakers becomes more robust, allowing ChatbouID distinguish H-H interaction and H-C interaction more reliably.
6 Conclusion
This work introduces novel framework named ChatbotID that effectively distinguishes between H- H and H-C dialogues by analysing interactional dynamics, particularly sentiment influence, using the GCT. ChatbotID demonstrates superior performance over existing methods across various datasets and LLM backbones.
Acknowledgments and Disclosure of Funding
This work is supported by the National Key Research and Development Program of China under grant DD4Y9C3307900; the National Natural Science Foundation of China under grants 62302 1 88, 62376103, 62436003 and 62206102; Major Science and Technology Project of Hubei Province under grant 2024BAA008; Hubei Science and Technology Talent Service Project under grant 2024DJC078: Ant Group through CCF-Ant Research Fund. The computation is completed in the HPC Platform of Huazhong University of Science and Technology.
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A Limitations
The experiments are conducted on clean, curated datasets without considering real-world noise. These factors may significantly impact the robustness of detection models when deployed in practical settings.
Noise-free Setting:
We assume that pre- pre-trained language models used in our benchmarking are well-suited for the detection task. However, these models were originally trained for language generation rather than detection, and suboptimal fine-tuning or domain mismatch may limit their effectiveness in distinguishing H-H and H-C dialogue.
Well-specifted Model:
Asymptotic Approximations: Some of the statistical analysis techniques employed rely on asymp- totic assumptions that require large sample sizes to achieve accurate estimation. In practice, especially with limited or imbalanced datasets, these approximations may not hold, potentially affecting the validity of the results.
Only Applicability to Two-Party Dialogues: Our current methodology and experimental validation exclusively focused two-party dialogues. While applying Granger Causality multi partti (N > 2) interactions is theoretically feasible, it introduces significant complexity. Specifically, the number of potential causal relationships to analyze grows quadratically from 2 to N * (N 1), making the current approach computationally challenging. Future work is required to extend our framework to model the more complex dynamics inherent in multi-party conversations, such as coalitions or mediation effects.
B Implementation Details
B.1 Hardware devices
All our experiments were meticulously conducted on a high-performance computing platform running Ubuntu. The platform is powered by an Intel(R) Xeon(R) Platinum 8176 CPU @ 2.10GHz, delivering robust computational capabilities. The system is equipped with substantial 503 GB of memory, ensuring efficient data processing and storage. Additionally, to further enhance computational power, we utilized four NVIDIA Corporation GA102GL RTX A6000 GPUs These GPUs provided the necessary parallel processing power to handle the intensive computational tasks associated with our research. The stability and broad support of the Ubuntu operating system allowed to fully leverage the hardware's 's performance, ensuring the smooth execution of experiments and the reliability of our results.
B.2 Datasets
DailyDialog: This dataset contains high-quality, multi-turn dialogues reflecting everyday human communication. The conversations cover various topics and exhibit natural lan- guage usage. We utilized dialogues directly from this corpus part of our H-H chit-chat data, selecting conversations exceeding a minimum turn length threshold suitable for GCT analysis.
This dataset consists of chit-chat dialogues where participants assigned specific persona profiles that they are expected to condition their conversation on. encourages engaging and consistent dialogue. Similar to DailyDialog, naturally occurring dialogues between human participants in this dataset were included in our H-H chit-chat corpus.
PersonaChat:
MuldWOZ A large-scale, multi-domain dataset for task-oriented dialogues, covering domains like restaurants, hotels, transportation, etc. is standard benchmark in dialogue state tracking and end-to-end dialogue systems. We used the numan-human Wizard-of- Oz collected dialogues within this dataset, where one human plays the user and another simulates a constrained system based on database information, as representative examples for our H-H task-oriented corpus.
Taskmaster-1: This dataset contains goal-oriented dialogues, covering tasks such as ordering pizza, creating auto repair appointments, and booking flights. includes both spoken and written conversations collected via Wizard-of-Oz setup. We specifically used the written
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dialogues where both the 'user' and the 'wizard' (simulating the system) were human participants to form part our H-H task-oriented corpus.
The Human-Human (H-H) corpus used in our experiments was formed by selecting dialogues directly from the aforementioned datasets (Daily Dialyo. PersonaChat, the buman-controllee segments of MultiWOZ, and Taskmaster-1 WoZ data). Dialogues below a pre-defined length (e.g., 20 turns) were filtered out to ensure suitability for Granger Causality analysis.
H-H Corpus:
H-M Corpus Construction: The Human-LLN, (H-M) corpus was constructed semi-synthetically, derived directly from the dialogues selected for the H-H corpus to ensure maximal compa- rability of user input and conversational context. For each selected H-H dialogue C {(U1, A1), (U2, A (where U, denotes a user utterance and A, denotes the original human agent's utterance at turn 1), identified all turns originally spoken by the human agent A. We then employed a specific pre-trained Large Language Model (LLM-X, e.g., specify model like Llama-2-Chat 70B or GPT-4) generate alternative responses for these turns.
Specifically, for each agent turn A,, the dialogue history preceding it, typically ending with the user's utterance U,, was provided (U1, Ai, U2 U2,A2,1.... A2....,,4f A,,1,U U,) where A4. are the previously generated LLM responses (or origi- nal Ag for k = 1 if the agent starts). The LLM prompted to generate suitable response A/ given this history:
context LLM-X. Let the history be He
A = LLM-X((H.
(13)
This generated response A then replaced the original human response A, in the dialogue sequence. This process was repeated for all agent turns in the dialogue, resulting in new H-M dialogue Cn0 {(U,, A1), (U2, ..), Note that the user utterances U, remain identical to those in the original H-H dialogue C(O)
For dialogues derived from PersonaChat, the corresponding persona information was included in the prompt for LLM-X to encourage consistent persona adoption. For task-oriented dialogues derived from MultWWOZ and T relevant task goals or simulated dialogue states (if available and applicable) were potentially included in the prompt history H, to guide the LLM towards task completion, mimicking the information available construction method yields an H-M corpus where the user's 's side of the conversation is natural human language drawn from established datasets, while the agent's side is generated by the target L.LM conditioned on that human input, allowing for controlled comparison of response patterns and interaction dynamics against the original H-H dialogues. Similar length filtering was applied to the resulting H-M dialogues.
the original human agent/wizard. This
B.3 Metrics
To ensure the accuracy and reliability of the results, each experiment was conducted in triplicate, and the standard deviations were calculated. This approach effectively assesses the stability and consistency of the data, thereby enhancing the credibility of our conclusions. To assess the detector's capability to differentiate between texts generated by large language models (LLMs)) and those written by humans, we utilize Accuracy (A) and the Area Under the Receiver Operating Characteristie Curve (AUROC) as primary performance metrics. Additionally, we consider other metrics, such as Fl scores (FI) and Recall (R), to provide more comprehensive evaluation.
TP TN A TP+TN FP+FN
(14)
TP
PxR
R FP+
FI -1=2X P+R
(15)
True Positives (TP) refer to H-H dialogue correctly identified by the model. True Negatives (TN) represent H-C dialogue accurately classified as H-c dialogue. False Positives (FP) denote H-C
dialogue incorrectly labeled H-H dialogue, while False Negatives (FN) correspond to H-H dialogue the model fails to identify correctly.
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Performance evaluation
C.1
Cross-Domain Evaluation
Table 5: Cross-domain evaluation results. All methods were trained exclusively on the Daily dataset and evaluated on the entirely unseen Taskmaster-1, 1, PersonaChat, and MultiWOZ test sets. The best number is highlighted in bold, while the second best one is underlined. Our approach consistently outperforms other methods.
Taskmaster-1
PersonaChat
MultiWZZ
Method
ACC
F1
ACC
F1
ACC
F1
DetectGPT (ICMI. 2023)
59.12 6082
55.66
1.54
60.70 0.98
59.39
1.31
61.30 3,99
61.78 2.15
COCO (EMNLP 2023)
66.80 =070
68.29
2.16
69.56
0.41
65.58
0.9
65.67 + 1.36
68.88 + 2.46
LLMDet (EMNLP 2023)
60.13 s 1.14
64.83
62.13
a 1.33
61.81
0.57
63.43 a 1.20
62.96 : 1.46
SeqXGPT (EMNLP 2023)
59.75 =091
64.66 0.99
58.48
+ 1.56
60.58
1.77
61.00 1.22
61.18
0.77
Fast-DetectGPT (ICLR 2024)
60.93 1.96
60.66 + 1:64
63.02
2.64
62.05
1.10
64.01 + 0.688
61.28
0.59
T5-Sentinel (EMNLP 2024)
69.24 1.31
70.07
0.16
74.95
0.42
72.41
1.45
74.90 0.94
71.67 2.57
RoBERTa-MPU (ACL 2024)
69.35 0993
74.15
0.56
1.29 73.51
72.15
2.30
70.99 + 4.83
74.79 0.96
DeTeCtive (NeurIPS 2024)
67.85 0.45
70.69
1.58
71.46 0.55
71.98
1.05
71.87 1.50
73.40 4.89
OUTFOX (AAAI 2024)
76.39 1.88
0.72 78.88
1.01 72.29
78.10
3.07
78. 64 0.87
76.70
a: 1.73
GFCScore (ACL 2025)
67.73 a 1.97
71.12 0.33
73.85
1.99
0.22 73.98
71.82 à 3.20
65.30 1,20
GPT-3.5-turbo (2023)
60.72 1.73
0.95 59.28
59.38
20.4
61.14 1.09
63.07 1:20
60.18 :: 1.88
LLaMA-7B (2024)
2
60.54
1.76
60.36
1.56
61.76 0.78
58.05 #2.14
LLaMA-13B (2024)
60.94 23.30
62.81 +
63.32 0.26
63.53
#285
65.16
0.30
62.35 1.13
GPT-4 (2024)
58.90 =
1.64 64.61
64.23 1.25
59.87
0000
67.70 0.43
63.29 0.99
Gemma (2025)
62.26 0014
1.01 66.68
66.87 1.00
62.98
3.70
67.98 0.76
62.12 + 1,51
Qwen-2 (2025)
59.39 -071
61.67
2:43
64.81
0.05
61.78
3.62
64.98 0:90
62.67 + 1,05
Deepseek-RR RI (2025)
63.29 1.99
64.73
1.09
65.22
2.25
66.46
1.84
66.27
a 0.12
64.62 à 1.06
ChatbotlD (Ours)
80.59 1,18
81.96
2.30
83.84 1.38
84.72
2.19
82.28
0.62
83.40 0.45
To test for robustness against domain shift, we performed a cross-domain evaluation. We trained ChatbotI and all baseline models exclusively on the DailyDialog dataset and evaluated their performance on the entirely unseen Taskmaster-l, PersonaChat, and MultiWOZ test sets. General- purpose LLMs (e.g. GPT-3.5-turbo, LLaMA-7B, LLaMA-7B, LLaMA-133 Gemma, etc.) adopt zero-shot detection approach. The results of our cross cross-domain evaluation demonstrate the robustness of our approach. While all methods were trained exclusively on DailyDialog ChatbotID maintains high Fi-score of over 83% across all datasets. This performance represents substantial margin of 5-20% Fl points over all baseline methods.
C.2 Zero-Shot Evaluation on WildChat dataset
Table 6: Experimental results on the WildChat dataset.
Method
WildChat ACC
DetcctGPT (ICML 2023)
58.04 + 2.62
COCO (EMNLP 2023)
64.76 0.37
LLMDet (EMNLP 2023)
69.67 = 0.90
SeqXGPT (EMNLP 2023)
60.19 1.05
Fast-DetectGPT (ICLR 2024)
62.19 1.05
T5-Sentinel (EMNLP 2024)
68.03
RoBERTa-MPU (ACL. 2024)
67.68 2.97
DeTeCtive (NeurIPS 2024)
67.85
OUTFOX (AAAI 2024)
7.644
GECScore (ACL 2025)
68.44 0.60
ChatbotID (Ours)
79.12 + 1.53
To evaluate our model's performance on naturally occurring H-C dialogues, we tested our model in zero-shot setting on the WildChat dataset. ChatbotID achieves the highest accuracy (79.12%) among all methods, outperforming the most competitive baselines like OUTFOX. This result
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demonstrates that the unidirectional influence signal captured by ChatbotID is not merely an artifact of our semi-synthetic data generation process. Instead, is genuine and detectable characteristic present in real-world human-I.I.M interactions.
C.3 Inference Complexity Comparison
Table 7: Inference Complexity Comparison.
Method
Inference Complexity
DetcctGPT (ICML 2023)
(n))
COCO (EMNLP 2023)
0(1)
LLMDet (EMNLP 2023)
((n)
SeqXGPT (EMNLP 2023)
0(1)
Fast-DetectGPT (ICLR 2024)
0(n)
T5-Sentinel (EMNLP 2024)
0(n)
RoBERTa-MPU (ACL 2024)
0(1)
DeTeCtive (NeurIPS 2024)
0(1)
OUTFOX (AAAI 2024)
0(n)
GECScore (ACL 2025)
O(n)
ChatbotID (Ours)
0(1)
In the training phase, we acknowledge that our proposed method, ChatbotID, has higher computa- tional overhead compared to some lightweight detection approaches. The computational complexity is primarily concentrated in two stages. The calculation of GCT features requires additional processing time. The fine-tuning process, which incorporates auxiliary losses, is slightly more complex than a standard single-task classification setup. However, the primary advantage of ChatbotID lies in its inference efficiency. Once trained, making prediction is extremely fast, achieving an inference complexity (1). This is because it only requires a single forward pass through the model to make prediction. This stands in contrast to cntturbation-based approaches, such as DetectGPT, LLMDet, and OUTFOX, which are computationally heavy at inference time. For every single dialogue they need to evaluate, they must perform multiple forward passes through LLM to generate perturbations and calculate scores. This characteristic makes them prohibitively slow and expensive for any real-time large-scale application.
D Potential Positive Societal Impacts
Enhanced Dialogue Understanding and Interaction: By leveraging interaction dynamics and semantic-focused attribution, this research aims to improve dialogue understanding and classification accuracy beyond purely semantic analysis. This could lead to more effective communication tools, such as chatbots and virtual assistants, enhancing user experience and satisfaction across various applications.
The development of sophisticated models for detecting machine-generated text can play a crucial role in combating misinformation and ensuring content authenticity. In where L.LM-generated is becoming increasingly prevalent, having reliable methods to distinguish between human and Al-generated texts is vital for maintaining trust in digital communications.
Improved Detection of AI-Generated Text:
Promotion of Ethical Use of AI: Through advancements identifying supports the ethical use of technology by helping prevent misuse and manipulation. It contributes to the broader conversation on Al ethics and responsibility, encouraging transparency and accountability in how AI technologies deployed and managed.
this research
26


Reviews:
[{"review_id": "cgbqEDaWnW", "reviewer": "Reviewer_8RTu", "summary": "This paper presents an approach, ChatbotID, for identifying\nhuman-chatbot dialogues vs. human-human dialogues.  It is motivated by\nthe observation that human-human dialogues exhibit linguistic style\naccommodation while human-chatbot dialogues do not.  The approach\ninvolves fine-tuning an LLM using Multi-Task Learning using a\ncombination of features that encode time-series based linguistic\naspects of the dialogue and features calculated using the Granger\nCausality Test (GCT) that capture interaction dynamics relationships\nbetween the participants.", "questions": "1. The data that ChatbotID is being tested on is drawn from the datasets used for training.  Shouldn't entirely new dialogues be used, i.e.,  dialogues from entirely new sources? \n\n2. Are the methods that are compared to ChatbotID been given access to the same training data used to train ChatbotID?  \n\n3. Why does the H-C corpus have to include the same human utterances as the H-H corpus?  Stated differently, the method used to create the H-C corpus will result in unrealistic dialogues that will not reflect the human-chatbot dialogues that actually occur on-line.\n\n4. Does the L_G component of the loss assume that all dialogues in the dataset are from the same type of user/agent source combination — either H-H or H-C?  How is L_G computed at test time, i.e., for an arbitrary dialogue that is not part of one of the training datasets?", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. The approach proposed is novel. And although the use of features\n   that capture linguistic style accommodation in dialogues is not new\n   (see below), it is a really nice idea to incorporate into methods\n   trying to detect chatbots in online dialogues.  I have also not\n   seen the GCT statistical framework used to extract time series\n   features for dialogues.  It is a really nice idea.\n\n2. The problem addressed is also an important one.\n\n3. The experiments are strong in that ChatbotID is compared to many\n   alternatives using (depending on how you count) three (or one)\n   datasets.  The paper also provides a nice set of ablations with\n   respect to the three components of the loss function.\n\nWeaknesses\n\n1. The paper lacks references to the body of work in NLP on linguistic\n   style accommodation and its use in various classification tasks.\n   So the paper's first stated contribution, related work, and\n   motivation section will need to be modified to reflect this.\n\nFrom NLP:\n\nDanescu-Niculescu-Mizil, C., Gamon, M., & Dumais, S. (2011,\nMarch). Mark my words! Linguistic style accommodation in social\nmedia. In Proceedings of the 20th international conference on World\nwide web (pp. 745-754).\n\nMao and Guy Lebanon.  2007. Isotonic conditional random fields and\nlocal sentiment flow. In Advances in Neural Information Processing\nSystems.\n\nDanescu-Niculescu-Mizil, C., Lee, L., Pang, B., & Kleinberg, J. (2012,\nApril). Echoes of power: Language effects and power differences in\nsocial interaction. In Proceedings of the 21st international\nconference on World Wide Web (pp. 699-708).\n\nTan, C., Niculae, V., Danescu-Niculescu-Mizil, C., & Lee, L. (2016,\nApril). Winning arguments: Interaction dynamics and persuasion\nstrategies in good-faith online discussions. In Proceedings of the\n25th international conference on world wide web (pp. 613-624).\n\nWang, Lu, and Claire Cardie. \"A Piece of My Mind: A Sentiment Analysis\nApproach for Online Dispute Detection.\" In Proceedings of the 52nd\nAnnual Meeting of the Association for Computational Linguistics\n(Volume 2: Short Papers), pp. 693-699. 2014.\n\n\nFrom Sociolinguistics (possibly better references than the Dunbar et\nal. paper):\n\nH. Giles. Communication accommodation theory. In\nEngaging theories in interpersonal communication:\nmultiple perspectives. Sage Publications, 2008.\n\nH. Giles, J. Coupland, and N. Coupland.\nAccommodation theory: Communication, context, and\nconsequences. In Contexts of accommodation:\ndevelopments in applied sociolinguistics. Cambridge\nUniversity Press, 1991\n\n2. If I understood the paper correctly, I think that there are\n   problems with the evaluation ---\n\n   (a) the test data does not seem appropriate.  The data that\n   ChatbotID is being tested on is drawn from the datasets used for\n   training.  Shouldn't entirely new dialogues be used, i.e.,\n   dialogues from entirely new sources? In particular, section 5.1\n   states that the H-H and H-C corpora \"ensure comparability in style\n   and domain\", whereas we'd like there to be a variety of styles and\n   domains in the data in order to reflect the actual variety of\n   dialogues that occur on-line.\n\n   (b) the methods that are compared to ChatbotID do not appear to\n   have been given access to the training data used to train\n   ChatbotID,\n\n   (c) The H-C corpus won’t reflect real human-LLM dialogues.  It\n   would be better to train on naturally occurring H-C dialogues.  In\n   the same vein, the approach should be tested on naturally occurring\n   H-C dialogues. One possibility might be the H-C dialogues in the\n   WildChat dataset. [Zhao, Wenting, et al. \"WildChat: 1M ChatGPT\n   Interaction Logs in the Wild.\" The Twelfth International Conference\n   on Learning Representations. 2024]\n\n3. Some aspects of the approach are not clear; namely, the third\n   component of the loss function, L_G, seems to be calculated based\n   on the entire dataset rather than for a particular dialogue. But\n   the overall goal is to classify a single dialogue.  To use this\n   component of the loss, in practice, means that the approach is\n   assuming that all dialogues in the dataset are from the same type\n   of user/agent source combination — either H-H or H-C.  Is this\n   correct?  And the value of L_G is exactly the same across all\n   dialogue examples in the test set?  How is L_G computed at test\n   time, i.e., for an arbitrary dialogue that is not part of one of\n   the training datasets? Finally, does the approach require the\n   binary GCT significance vectors at test time (and how would they be\n   calculated)?", "was_revised": true, "final_justification": "Given the author responses in the rebuttal and the promised changes, the paper proposes a novel approach to an important, complex and unsolved problem.", "rebuttal": "We extend our sincere gratitude for your thorough and insightful review of our manuscript. Your feedback is invaluable and has identified several key areas for improvement and clarification. We agree with the points you have raised and will incorporate corresponding revisions in our final manuscript.\n\n\n### **Question 1: Missing References**\n\nWe thank you for reminding us of this important point. We have already added these related references in the manuscript as references.\n\n1. Revised Motivation. We now begin the motivation by explicitly acknowledging the foundational research on linguistic accommodation and interaction dynamics in human-human (H-H) dialogues [1, 2, 3, 4, 5]. We use this established literature as a starting point to introduce our core hypothesis: human-chatbot (H-C) dialogues exhibit a fundamentally different, structurally unidirectional influence, which contrasts with the typically reciprocal dynamics studied in prior work. \n\n2. Refined Contribution. Motivated by Communication Accommodation Theory, this study systematically reveals two principal patterns of dialogues: H-H interactions are characterized by substantial bidirectional sentiment exchange, whereas H-C interactions demonstrate a distinct asymmetric influence.\n\n3. Expanded Related Work. We have added a new subsection, Linguistic Accommodation and Interaction Dynamics, to the Related Work chapter. In this section, we now thoroughly discuss the cited literature on accommodation theory [6, 7, 8] and its application in NLP for tasks like power detection [3] and persuasion analysis [4]. This allows us to credit prior work appropriately and more delineate how our method's use of GCT moves beyond the correlation-based metrics standard in those studies.\n\n\n[1]. Danescu-Niculescu-Mizil C, Gamon M, Dumais S. Mark my words! Linguistic style accommodation in social media[C].\n\n[2]. Mao Y, Lebanon G. Isotonic conditional random fields and local sentiment flow[J]. \n\n[3]. Danescu-Niculescu-Mizil C, Lee L, Pang B, et al. Echoes of power: Language effects and power differences in social interaction[C].\n\n[4]. Tan C, Niculae V, Danescu-Niculescu-Mizil C, et al. Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions[C]. \n\n[5]. Lu Wang and Claire Cardie. A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection. \n\n[6]. Dragojevic M, Gasiorek J, Giles H. Communication accommodation theory[J]. \n\n[7]. Giles, H. (2008). Communication accommodation theory. In L. A. Baxter & D. O. Braithewaite (Eds.), *Engaging theories in interpersonal communication: Multiple perspectives*. \n\n[8]. H. Giles, J. Coupland, and N. Coupland. Accommodation theory: Communication, context, and consequences. In Contexts of accommodation: developments in applied sociolinguistics. \n\n### **Question 2: Concerns about the Evaluation Methodology**\n\n1. Clarification of our evaluation.\n\nFor each dataset, we partition the dialogues into a training set (70%), a validation set (10%), and a held-out test set (20%). We ensure there is no overlap at the dialogue level between these splits. All performance metrics reported in our main results tables are computed exclusively on the unseen test set.\n\n2. Cross-Domain Evaluation\n\nTo test for robustness against domain shift, we performed a cross-domain evaluation. We trained ChatbotID and all baseline models exclusively on the DailyDialog dataset and evaluated their performance on the entirely unseen Taskmaster-1, PersonaChat, and MultiWOZ test sets. General-purpose LLMs (e.g. GPT-3.5-turbo, LLaMA-7B, LLaMA-7B, LLaMA-13B, Gemma, etc.) adopt a zero-shot detection approach.\n\n---\n|Method|Taskmaster-1 ACC|Taskmaster-1 F1|PersonaChat ACC|PersonaChat F1|MultiWOZ ACC|MultiWOZ F1|\n|---|---|---|---|---|---|---|\n|DetcctGPT (ICML 2023)|59.12±0.82|55.66±1.54|60.70±0.98|59.39±1.31|61.30±3.69|61.78±2.75|\n|COCO (EMNLP 2023)|66.80±0.70|68.29±2.16|69.56±0.41|65.58±0.93|65.67±1.36|68.88±2.46|\n|LLMDet (EMNLP 2023)|60.13±1.14|64.83±2.37|62.13±1.33|61.81±0.57|63.43±1.20|62.96±1.46|\n|SeqXGPT (EMNLP 2023)|59.75±0.91|64.66±0.99|58.48±1.56|60.58±1.77|61.00±1.22|61.18±0.77|\n|Fast-DetectGPT (ICLR 2024)|60.93±1.96|60.66±1.64|63.02±2.64|62.05±1.10|64.01±0.68|61.28±0.59|\n|T5-Sentinel (EMNLP 2024)|69.24±1.31|70.07±0.16|74.95±0.42|72.41±1.45|74.90±0.94|71.67±2.57|\n|RoBERTa-MPU (ACL 2024)|69.35±0.93|74.15±0.56|73.51±1.29|72.15±2.30|70.99±4.82|74.79±0.96|\n|DeTective (NeurIPS 2024)|67.85±0.45|70.69±1.58|71.46±0.55|71.98±1.05|71.87±1.30|73.40±4.89|\n|OUTFOX (AAAI 2024)|76.39±1.88|78.88±0.72|72.29±1.01|78.10±3.07|78.64±0.87|76.70±1.73|\n|GECScore (ACL 2025)|67.73±1.97|71.12±0.38|73.85±1.99|73.98±0.22|71.82±3.20|65.30±1.20|\n|GPT-3.5-turbo (2023)|60.72±1.73|59.28±0.95|59.38±0.14|61.14±1.09|63.07±1.20|60.18±1.88|\n|LLaMA-7B (2024)|59.74±1.23|58.85±1.17|60.54±1.76|60.36±1.56|61.76±0.78|58.05±2.14|\n|LLaMA-13B (2024)|60.94±3.30|62.81±2.27|63.32±0.26|63.53±2.85|65.16±0.30|62.35±1.13|\n|GPT-4 (2024)|58.90±2.31|64.61±1.64|64.23±1.25|59.87±0.60|67.70±0.43|63.29±0.98|\n|Gemma (2025)|62.26±0.14|66.68±1.01|66.87±1.00|62.98±3.70|67.98±0.76|62.12±1.51|\n|Qwen-2 (2025)|59.39±0.71|61.67±2.43|64.81±0.05|61.78±3.62|64.98±0.90|62.67±1.05|\n|Deepseek-R1 (2025)|63.29±1.99|64.73±1.69|65.22±2.23|66.46±1.84|66.27±0.12|64.62±1.06|\n|ChatbotID (Ours)|80.59±1.18|81.96±2.30|83.84±1.38|84.72±2.19|82.28±0.62|83.40±0.45|\n---\n\nThe results of our cross-domain evaluation demonstrate the robustness of our approach. While all methods were trained exclusively on DailyDialog, ChatbotID maintains a high F1-score of over 83% across all datasets. This performance represents a substantial margin of 5-20% F1 points over all baseline methods.  \n\n3. Zero-Shot Evaluation on WildChat dataset.\n\nWe thank the reviewer for the excellent suggestion to use the WildChat dataset. To evaluate our model's performance on naturally occurring H-C dialogues, we tested our model in a zero-shot setting on the WildChat dataset.\n\n|Method|WildChat ACC|\n|---|---|\n|DetcctGPT (ICML 2023)|58.04±2.62|\n|COCO (EMNLP 2023)|64.76±0.37|\n|LLMDet (EMNLP 2023)|69.67±0.90|\n|SeqXGPT (EMNLP 2023)|60.19±1.05|\n|Fast-DetectGPT (ICLR 2024)|62.19±1.05|\n|T5-Sentinel (EMNLP 2024)|68.03±1.24|\n|RoBERTa-MPU (ACL 2024)|67.68±2.97|\n|DeTective (NeurIPS 2024)|67.85±0.45|\n|OUTFOX (AAAI 2024)|78.44±0.60|\n|GECScore (ACL 2025)|68.44±0.60|\n|ChatbotID (Ours)|79.12±1.53|\n---\n\nThe results validate the robustness of our approach. ChatbotID achieves the highest accuracy (79.12%) among all methods, outperforming even the most competitive baselines like OUTFOX. This result demonstrates that the unidirectional influence signal captured by ChatbotID is not merely an artifact of our semi-synthetic data generation process. Instead, it is a genuine and detectable characteristic present in real-world human-LLM interactions. \n\n\n4. On the fairness of comparison with baseline methods.\n\nWe apologize that our experimental setup was not described with sufficient clarity. Your review is correct that all methods must be trained on the same data for a fair comparison. We confirm that this was indeed our methodology. To remove any ambiguity, we have revised the description of our baseline setup in Baselines to state explicitly:\n\n*To ensure a fair and rigorous comparison, we re-implemented all baseline models and re-trained them from scratch on the same training corpus used for  ChatbotID. All reported performance metrics for these baselines are from this controlled setup, ensuring that any performance differences are attributable to the methods' underlying approaches rather than variations in training data. General-purpose LLMs (e.g., GPT-3.5-turbo, LLaMA-7B, Qwen-2, etc.) adopt a zero-shot detection approach.*\n\n\n### **Question 3: Lack of Clarity on the $L_G$ Loss Component**\n\nWe apologize that our $L_G$ was not described with sufficient clarity.\nThe following is a clear explanation of the $L_G$ Loss Component, which we will integrate into the revised paper:\n\n1. **Granularity of $L_G$ Calculation:** The $L_G$ loss is computed on a **per-dialogue basis**, not on the entire dataset. During training, for each dialogue $C^{(i)}$ in a batch, the following steps are performed:\n- Its GCT feature vector $V_{GCT}^{(i)}$ is computed.\n- $V_{GCT}^{(i)}$ is converted into a binary GCT significance vector $b^{(i)}$.\n- The model predicts a corresponding probability vector $\\hat{b}^{(i)}$.\n- The $L_G$ loss is calculated based on the comparison between $b^{(i)}$ and $\\hat{b}^{(i)}$ (e.g., using binary cross-entropy), and then averaged over all samples in the batch. Thus, the value of $L_G$ can differ for each batch.\n\n2.  **Dataset Type:** The method does not assume that all dialogues in the dataset are of the same type (i.e., all H-H or all H-C). A training batch can contain a mix of both H-H and H-C dialogues. The purpose of $L_G$ is to serve as an auxiliary task that forces the model to learn to predict the intrinsic interaction dynamics of any given dialogue, regardless of its final classification label.\n\n3. **Computation at Test Time**: $L_G$ is an **auxiliary loss used only during the training phase.** Its purpose is to guide the model to learn more discriminative feature representations. During testing, the $L_G$ loss function and its corresponding prediction head are completely discarded and do not participate in any computation.\n\n\n### **Question4: Paper Formatting Concerns**\n\nWe apologize that the figures in the manuscript were unreadable. In the final version, we will **regenerate all figures and charts to ensure they have a higher resolution, larger font sizes, and clearer legends** for improved readability.\n\nWe sincerely thank you for your constructive feedback. We hope that our response has addressed your concerns."}, {"review_id": "kHmNDlbL3x", "reviewer": "Reviewer_6f6s", "summary": "The paper proposes a method for detecting human-human dialogs versus human-llm/machine dialogs. It does so based on a combination of engineered features and LLMs to estimate feedback, as well as a statistical time series test (Granger causality). Results are given on 4 publicly available human-human dialog datasets, with the machine side for the experiments generated by a variety of recent sota LLMs. These indicate that the proposed method is able to detect dialogs with machine utterances at a meaningfully improved rate compared to existing methods.", "questions": "* What is the impact of turns? Does it get more accurate with more turns and history? Results at each turn in the conversation, or in buckets (first 5 turns, 10 turns, etc) would be helpful. It's plausible that there is a lot of variation here. \n* Does this only work for 2 party dialogs? What happens if a 3rd party is involved?", "limitations": "n/a", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The paper is well written. It introduces a seemingly helpful statistic/feature for this \"bot detection\" task. It is an iterative work on a clearly important and already established task. But it presents sufficient evidence that the proposed features and method is effective. \nIt does have several limitations however. It is computationally heavy, with several features needing to be calculated, before then fine tuning with a set of custom losses. \n\nThere is a lot of focus in the introduction and title of the paper about the Granger causality test. The proposed method does not work nearly as well though without all the other features. The ablation study is of course required here, and it is helpful. But for lack of a softer way of saying it, the current title of the paper is more alluring than e.g. \"good feature engineering for bot detection\" which may be more accurate.", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for reviewing our manuscript and providing valuable feedback. We agree with the points you have raised and will incorporate corresponding revisions in our final manuscript.\n\n### **Question1: Computational complexity**\n\nWe sincerely thank the reviewer for raising this practical and important point regarding the computational cost of ChatbotID.\n\nIn the training phase, we acknowledge that the method has a higher computational overhead than some lightweight detection approaches. The Computational complexity is primarily concentrated in two stages:\n\n- Feature Pre-computation: The calculation of GCT features requires additional processing time.\n- Multi-task Fine-tuning. Fine-tuning with auxiliary losses is slightly more complex than a standard single-task classification.\n\nHowever, once ChatbotID is trained, making a prediction is extremely fast. As shown in the table, ChatbotID achieves $O(1)$ inference complexity. Once trained, it requires only a single forward pass through the model to make a prediction. Perturbation-based approaches, such as  **DetectGPT**, **LLMDet**, and **OUTFOX**, are computationally heavy at inference time. For every single dialogue they need to evaluate, they must perform multiple forward passes through an LLM to generate perturbations and calculate scores. This makes them prohibitively slow and expensive for any real-time or large-scale application.\n\n| Method                     | Inference Complexity |\n| -------------------------- | -------------------- |\n| DetcctGPT (ICML 2023)      | $O(n)$             |\n| COCO (EMNLP 2023)          | $O(1)$             |\n| LLMDet (EMNLP 2023)        | $O(n)$             |\n| SeqXGPT (EMNLP 2023)       | $O(1)$             |\n| Fast-DetectGPT (ICLR 2024) | $O(n)$             |\n| T5-Sentinel (EMNLP 2024)   | $O(n)$             |\n| RoBERTa-MPU (ACL 2024)     | $O(1)$             |\n| DeTective (NeurIPS 2024)   | $O(1)$             |\n| OUTFOX (AAAI 2024)         | $O(n)$             |\n| GECScore (ACL 2025)        | $O(n)$             |\n| ChatbotID (Ours)           | $O(1)$             |\n---\n\nWhile we acknowledge the higher upfront training cost, the resulting $O(1)$ inference efficiency is a significant practical advantage of our work. We are grateful to the reviewer for prompting us to highlight and formalize this important strength.\n\n### **Question2: The Paper's Title**\n\nWe sincerely thank you for this insightful comment.  The performance of ChatbotID is indeed a result of the synergy within the entire framework. We chose to emphasize GCT because it represents the most central and innovative contribution of our methodology. However, to more accurately reflect the full scope of our work, we will modify the title. For instance, a title such as **ChatbotID: Identifying Chatbots with Causal and Semantic Consistency**.\n\n1. **Causal Interaction Consistency ($L_G$).** It models the **dynamics of influence** between participants, specifically how one is emotional or stylistic cues causally affect the other's subsequent responses. Human-human dialogues exhibit a natural, often subtle, causal flow. As our analysis shows, chatbots often fail to replicate this dynamic, resulting in a weaker causal link. We consider this the emotional and dynamic consistency of the dialogue.\n2. **Semantic Content Consistency ($L_S$).** Our Semantic-Focused Attribution Supervision captures this. It addresses the **coherence of the content itself**, whether the dialogue is logical, factually sound, and free of commonsense violations. While a chatbot might be grammatically perfect, it often fails at this deeper semantic level over a longer interaction. We consider this the \"logical and factual consistency\" of the dialogue.\n\nFurthermore, in the introduction and conclusion, we will more clearly articulate that while GCT is the core innovation, the final performance is achieved through the synergistic fusion of semantic features, dynamic features, and our multi-task learning framework.\n\n### **Question 3: Ablation study**\nWe have conducted ablation experiments. As shown in Table 3, the performance of ChatbotID is indeed a result of the synergy within the entire framework. The baseline model, relying solely on the classification loss $L_{class}$, establishes a foundational level of performance across datasets. The introduction of the semantic-focused attribution supervision $L_S$ yields consistent accuracy improvements (e.g., from 63.19% to 67.58% on DailyDialog), demonstrating the value of guiding the model to recognize specific semantic deficiencies often present in chatbots. The integration of the causal interaction dynamics supervision using $L_G$ provides a more substantial boost in accuracy (e.g., from 63.19% to 70.99% on DailyDialog when combined with $L_{class}$. \n\n### **Question 4: The Impact of Dialogue Turns**\n\nWe are very grateful to the reviewer for this insightful question. You raise a crucial point: the model's performance varies with the length of the dialogues. It is indeed plausible that the strength of the detection signal changes as the dialogue history grows. We partitioned our test set into several buckets based on the number of turns in the dialogues and evaluated the accuracy of ChatbotID within each bucket.\n\n---\n\n| Turns | DailyDialog ACC | DailyDialog F1 | PersonaChat ACC | PersonaChat F1 | MultiWOZ ACC | MultiWOZ F1 |\n| ----- | --------------- | -------------- | --------------- | -------------- | ------------ | ----------- |\n| 1-5   | 60.07±0.38     | 62.39±0.98    | 61.29±1.97     | 61.86±0.51    | 60.89±0.81  | 63.26±1.11 |\n| 6-10  | 75.88±0.60     | 78.12±1.48    | 76.90±3.36     | 75.96±0.27    | 78.96±1.50  | 78.34±0.48 |\n| 10-15 | 83.41±0.66     | 82.83±1.93    | 84.96±0.54     | 82.44±0.83    | 84.86±3.08  | 83.72±2.53 |\n| 15+   | 86.82±0.33     | 85.49±1.45    | 86.95±1.24     | 83.91±1.95    | 89.17±2.75  | 84.77±0.29 |\n\n---\n\nIn the initial stages of the dialogues, from 1-5 turns up to 10-15 turns, the model exhibits a dramatic and consistent improvement in both accuracy and F1-score across all three datasets. For instance, on the MultiWOZ dataset, accuracy skyrockets from 60.89% in the 1-5 turn bucket to 84.86% in the 10-15 turn bucket. In very short dialogues, there is insufficient interaction history to establish a stable pattern of influence. As turns accumulate, the cause-and-effect chain between speakers becomes more robust, allowing ChatbotID to distinguish  H-H interaction and H-C interaction more reliably. Interestingly, once the dialogues exceed 15 turns, the performance gains begin to plateau, indicating a signal saturation point. The model has gathered sufficient evidence to make a high-confidence determination, and further turns primarily offer redundant confirmation rather than new critical information.\n\n### **Question 5: Applicability to Multi-Party Dialogues**\n\nYou have correctly identified the scope of our current work. Our present methodology and experimental design are indeed **developed for two-party dialogues**. While extending GCT to multi-party (N-party, N > 2) dialogues is theoretically feasible, it introduces significant complexity. For instance, the number of causal pairs to consider would increase from 2 ($A→B$, $B→A$) to $N*(N-1)$, and more complex interaction patterns (e.g., coalitions, mediation effects) would need to be modeled. We will explicitly state this in the **Limitations ** of the paper. We will clarify that the current version of the GCD framework is designed for two-party dialogues. Extending it to multi-party dialogues is an important and challenging direction for future research. This would require extending the GCT framework or employing more advanced methods.\n\nWe sincerely thank you for your constructive feedback. We hope that our response has addressed your concerns."}, {"review_id": "7sJz0L0LrH", "reviewer": "Reviewer_26KV", "summary": "ChatbotID introduces a novel framework for distinguishing human-human and human-chatbot dialogues by leveraging Granger Causality-based interaction features and contextual embeddings, achieving improved detection accuracy across multiple datasets.", "questions": "see above", "limitations": "partially", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is the first to introduce GCT into the dialogue-based chatbot detection task. This moves beyond static linguistic features and is grounded in solid statistical theory, offering an insightful direction.\n\n2. The integration of semantic embeddings and interaction-based causal features via a multi-task learning framework is well-motivated and effectively implemented. The architecture is clear and theoretically sound.\n\n3. The model is evaluated on diverse datasets and across multiple LLM backbones, with detailed ablation studies that convincingly demonstrate robustness and applicability. \n\nWeaknesses: \n\n1. The paper repeatedly uses the term “significant” (dozens of times) to describe improvements, yet provides no statistical significance testing. This weakens the rigor of the claims, especially when some metrics in the tables show only marginal gains.\n\n2, The paper compares with strong baselines like DeTeCtive and OUTFOX, but fails to evaluate on the original datasets used by those methods. This undermines the fairness and rigor of the comparison and makes it difficult to assess whether the proposed method truly outperforms state-of-the-art approaches under standard benchmark settings.\n\n3. Granger Causality requires regression-based modeling and repeated significance testing across dialogue turns, which introduces non-trivial computation costs, making the method less suitable for real-time or large-scale deployment without optimization.\n\n4. The model is not tested against adversarially generated or obfuscated chatbot responses (e.g., prompt injection, paraphrased bots), which are highly relevant in realistic use cases.\n\n5. The semantic deficiency labels are generated via LLM self-evaluation based on prompted heuristics. This could introduce noise or inconsistency in the supervision signal.", "was_revised": true, "final_justification": "Thanks for the rebuttal and I'll keep my positive score.", "rebuttal": "We thank you for your detailed review and insightful comments on our work. We agree with the points you have raised and will incorporate corresponding revisions in our final manuscript.\n\n### **Question 1: Imprecise Word**\n\nWe are grateful to the reviewer for pointing out our imprecise word, and we apologize for having used the term 'significant'. We have meticulously reviewed the entire manuscript and have removed all instances of evaluative terms such as \"significant,\" \"substantial,\" \"huge,\" and \"considerable\".\n\n### **Question 2: Concerns about the Evaluation Methodology**\n\n1. On the fairness of comparison with baseline methods.\n\nWe apologize that our experimental setup was not described with sufficient clarity. Your review is correct that all methods must be trained on the same data for a fair comparison. We confirm that this was indeed our methodology. To remove any ambiguity, we have revised the description of our baseline setup in Baselines to state explicitly:\n\n*To ensure a fair and rigorous comparison, we re-implemented all baseline models and re-trained them from scratch on the same training corpus used for  ChatbotID. General-purpose LLMs (e.g., GPT-3.5-turbo, LLaMA-7B, Qwen-2, etc.) adopt a zero-shot detection approach.*\n\n2. Cross-Domain Evaluation\n\nTo test for robustness against domain shift, we performed a cross-domain evaluation. We trained ChatbotID and all baseline models exclusively on the DailyDialog dataset and evaluated their performance on the entirely unseen Taskmaster-1, PersonaChat, and MultiWOZ test sets. General-purpose LLMs (e.g. GPT-3.5-turbo, LLaMA-7B, LLaMA-7B, LLaMA-13B, Gemma, etc.) adopt a zero-shot detection approach.\n\n---\n|Method|Taskmaster-1 ACC|Taskmaster-1 F1|PersonaChat ACC|PersonaChat F1|MultiWOZ ACC|MultiWOZ F1|\n|---|---|---|---|---|---|---|\n|DetcctGPT (ICML 2023)|59.12±0.82|55.66±1.54|60.70±0.98|59.39±1.31|61.30±3.69|61.78±2.75|\n|COCO (EMNLP 2023)|66.80±0.70|68.29±2.16|69.56±0.41|65.58±0.93|65.67±1.36|68.88±2.46|\n|LLMDet (EMNLP 2023)|60.13±1.14|64.83±2.37|62.13±1.33|61.81±0.57|63.43±1.20|62.96±1.46|\n|SeqXGPT (EMNLP 2023)|59.75±0.91|64.66±0.99|58.48±1.56|60.58±1.77|61.00±1.22|61.18±0.77|\n|Fast-DetectGPT (ICLR 2024)|60.93±1.96|60.66±1.64|63.02±2.64|62.05±1.10|64.01±0.68|61.28±0.59|\n|T5-Sentinel (EMNLP 2024)|69.24±1.31|70.07±0.16|74.95±0.42|72.41±1.45|74.90±0.94|71.67±2.57|\n|RoBERTa-MPU (ACL 2024)|69.35±0.93|74.15±0.56|73.51±1.29|72.15±2.30|70.99±4.82|74.79±0.96|\n|DeTective (NeurIPS 2024)|67.85±0.45|70.69±1.58|71.46±0.55|71.98±1.05|71.87±1.30|73.40±4.89|\n|OUTFOX (AAAI 2024)|76.39±1.88|78.88±0.72|72.29±1.01|78.10±3.07|78.64±0.87|76.70±1.73|\n|GECScore (ACL 2025)|67.73±1.97|71.12±0.38|73.85±1.99|73.98±0.22|71.82±3.20|65.30±1.20|\n|GPT-3.5-turbo (2023)|60.72±1.73|59.28±0.95|59.38±0.14|61.14±1.09|63.07±1.20|60.18±1.88|\n|LLaMA-7B (2024)|59.74±1.23|58.85±1.17|60.54±1.76|60.36±1.56|61.76±0.78|58.05±2.14|\n|LLaMA-13B (2024)|60.94±3.30|62.81±2.27|63.32±0.26|63.53±2.85|65.16±0.30|62.35±1.13|\n|GPT-4 (2024)|58.90±2.31|64.61±1.64|64.23±1.25|59.87±0.60|67.70±0.43|63.29±0.98|\n|Gemma (2025)|62.26±0.14|66.68±1.01|66.87±1.00|62.98±3.70|67.98±0.76|62.12±1.51|\n|Qwen-2 (2025)|59.39±0.71|61.67±2.43|64.81±0.05|61.78±3.62|64.98±0.90|62.67±1.05|\n|Deepseek-R1 (2025)|63.29±1.99|64.73±1.69|65.22±2.23|66.46±1.84|66.27±0.12|64.62±1.06|\n|ChatbotID (Ours)|80.59±1.18|81.96±2.30|83.84±1.38|84.72±2.19|82.28±0.62|83.40±0.45|\n---\n\nThe results of our cross-domain evaluation demonstrate the robustness of our approach. While all methods were trained exclusively on DailyDialog, ChatbotID maintains a high F1-score of over 83% across all datasets.\n\n### **Question 3: Computational complexity**\n\nWe sincerely thank the reviewer for raising this practical and important point regarding the computational cost of ChatbotID.\n\nIn the training phase, we acknowledge that ChatbotID has a higher computational overhead than some lightweight detection approaches. The computational complexity is primarily concentrated in two stages:\n\n- Feature Pre-computation: The calculation of GCT features requires additional processing time.\n- Multi-task Fine-tuning. Fine-tuning with auxiliary losses is slightly more complex than a standard single-task classification.\n\nHowever, once ChatbotID is trained, making a prediction is extremely fast. As shown in the table, ChatbotID achieves $O(1)$ inference complexity. Once trained, it requires only a single forward pass through the model to make a prediction. Perturbation-based approaches, such as  **DetectGPT**, **LLMDet**, and **OUTFOX**, are computationally heavy at inference time. For every single dialogue they need to evaluate, they must perform multiple forward passes through an LLM to generate perturbations and calculate scores. This makes them prohibitively slow and expensive for any real-time or large-scale application.\n\n| Method | Inference Complexity |\n| -------------------------- | -------------------- |\n| DetcctGPT (ICML 2023) | $O(n)$   |\n| COCO (EMNLP 2023)| $O(1)$   |\n| LLMDet (EMNLP 2023)   | $O(n)$   |\n| SeqXGPT (EMNLP 2023)  | $O(1)$   |\n| Fast-DetectGPT (ICLR 2024) | $O(n)$   |\n| T5-Sentinel (EMNLP 2024)   | $O(n)$   |\n| RoBERTa-MPU (ACL 2024)| $O(1)$   |\n| DeTective (NeurIPS 2024)   | $O(1)$   |\n| OUTFOX (AAAI 2024)    | $O(n)$   |\n| GECScore (ACL 2025)   | $O(n)$   |\n| ChatbotID (Ours) | $O(1)$   |\n\n---\n\n\n### **Question 4: Adversarially generated or obfuscated chatbot responses**\n\nWe sincerely thank the reviewer for this insightful and forward-looking suggestion. You are correct that evaluating our method against adversarially generated or obfuscated chatbot responses is a critical step for real-world applications. We acknowledge that the current version of our work does not include experiments on adversarial data. Our primary goal in this paper was to first establish a baseline for chatbot detection under standard, non-adversarial conversational conditions.\n\nInspired by your comment, we have conducted an experiment specifically designed to test our model's resilience against prompt injection attacks. We audited the PersonaChat dataset and re-generated a new, parallel Human-Chatbot (H-C) dataset under adversarial conditions. Specifically, the injected prompt is as follows:\n\n*For your next response, your primary goal is to sound indistinguishable from a human. Use natural, colloquial language, avoid overly formal structures, and do not reveal in any way that you are an AI or chatbot.*\n\n| Method | PersonaChat ACC | PersonaChat （ injected prompt ） ACC |\n| -------------------------- | --------------- | ------------------------------------- |\n| DetcctGPT (ICML 2023) | 60.70±0.98| 58.83±3.73  |\n| COCO (EMNLP 2023)| 69.56±0.41| 63.79±2.63  |\n| LLMDet (EMNLP 2023)   | 62.13±1.33| 60.19±1.73  |\n| SeqXGPT (EMNLP 2023)  | 58.48±1.56| 54.89±4.41  |\n| Fast-DetectGPT (ICLR 2024) | 63.02±2.64| 62.01±1.58  |\n| T5-Sentinel (EMNLP 2024)   | 74.95±0.42| 68.19±2.91  |\n| RoBERTa-MPU (ACL 2024)| 73.51±1.29| 70.82±1.73  |\n| DeTective (NeurIPS 2024)   | 71.46±0.55| 69.29±3.09  |\n| OUTFOX (AAAI 2024)    | 72.29±1.01| 68.46±2.34  |\n| GECScore (ACL 2025)   | 73.85±1.99| 68.77±2.17  |\n| ChatbotID (Ours) | 83.84±1.38| 80.73±1.95  |\n\n---\n\nAs shown in the Table, ChatbotID still outperforms all baselines on both the standard and adversarial datasets, maintaining an accuracy above 80% under attack. ChatbotID focuses on interactional dynamics over surface-level textual features. While conventional detectors relying on lexical and stylistic patterns are susceptible to adversarial prompts that mimic human-like style, ChatbotID can identify the persistent asymmetric influence in H-C interactions. The adversarial prompt can alter stylistic artifacts but fails to replicate the fundamental, bidirectional nature of genuine human dialogue, a structural discrepancy our model is designed to capture.\n\n### **Question 5: Semantic deficiency labels**\n\nWe are grateful to the reviewer for this insightful comment. You have correctly identified a critical consideration in our methodology: the potential for noise or inconsistency in supervision signals generated via LLM-based self-evaluation. We agree that any automated labeling process is unlikely to be perfect.\n\nHowever, we argue that while the supervision signal for our semantic deficiency loss $L_S$ may contain some level of noise, it provides a meaningful learning objective that enhances our model's discriminative power. We provide direct empirical evidence for this claim through our ablation study.\n\n| Method| DailyDialog   | PersonaChat   | MultiWOZ | Taskmaster-1  |\n| ------------------------- | ------------- | ------------- | ------------- | ------------- |\n| $L_{class}$   | 63.19 ± 1.63 | 67.86 ± 1.97 | 69.16 ± 0.63 | 66.18 ± 4.93 |\n| $L_{class} + L_S$  | 67.58 ± 2.45 | 72.73 ± 0.79 | 74.71 ± 1.08 | 74.35 ± 1.59 |\n| $L_{class} + L_G$  | 70.99 ± 3.88 | 78.70 ± 8.55 | 77.70 ± 2.49 | 80.38 ± 1.29 |\n| $L_{class} + L_G + L_S$ | 80.23 ± 3.82 | 83.63 ± 1.74 | 87.01 ± 0.64 | 83.37 ± 2.70 |\n\n---\n\nThe improvement across multiple domains suggests that the supervision signal is far more signal than noise. If the labels were purely random or inconsistent, we would not expect to see such a systematic and positive impact on performance. The learning signal is guiding the model to recognize features that are genuinely indicative of LLM-generated text.\n\nWe sincerely thank you for your constructive feedback. We hope that our response has addressed your concerns."}]

Author rebuttal:

Meta-review: This paper introduces ChatbotID, a framework for distinguishing human-human and human-chatbot dialogues by combining Granger Causality Test (GCT)–based features from interactions with contextual embeddings. Experiments across multiple datasets show the effectiveness of the approach. 

Summary of Strengths:
- The paper is clearly written and addresses an important problem.
- The work introduces the novel idea of applying GCT to extract statistically principled features from interactions and uses these for chatbot detection.
- The integration of these features with contextual embeddings is well-motivated and clearly presented. 
- Experiments are detailed and include multiple datasets and LLMs. Ablation studies are useful.

Summary of Weaknesses:
- Test sets are from the same datasets as the training. The authors included cross-domain evaluation results during the rebuttals. It would be useful to include these in the paper.
- The work lacks appropriate references to prior work, a long list has been suggested by one of the reviewers. It would help to ensure that these are included in the paper.
- The method is computationally heavy due to repeated causal testing. Authors provided detailed inference complexity results during the rebuttals.

Given the additional results and analysis provided by the authors during the rebuttals, one of the reviewers who was engaged during the rebuttals increased their score. As also noted above, it would be good to see all of these additional pieces of information in the final paper.
Decision: Accept (poster)