Title: ChatbotID: Identifying Chatbots with Granger Causality Test
Venue: NeurIPS 2025
Accepted: yes
Keywords: Granger Causality

Dialogue

LLMs
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nChatbotID 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nsee above\n\nLIMITATIONS:\npartially", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nThe 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.\n\nQUESTIONS:\n* 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?\n\nLIMITATIONS:\nn/a", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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)?\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}