Probing the Uniquely Identifiable Linguistic Patterns of Conversational AI Agents (2024)

Abstract: The proliferation of Conversational AI agents (CAAs) has emphasised the need to distinguish between human and machine-generated texts, with implications spanning digital forensics and cybersecurity. While prior research primarily focussed on distinguishing human from machine-generated text, our study takes a more refined approach by analysing different CAAs. We construct linguistic profiles for five CAAs, aiming to identify Uniquely Identifiable Linguistic Patterns (UILPs) for each model using authorship attribution techniques. Authorship attribution (AA) is the task of identifying the author of an unknown text from a pool of known authors. Our research seeks to answer crucial questions about the existence of UILPs in CAAs, the linguistic overlap between various text types generated by these models, and the feasibility of Authorship Attribution (AA) for CAAs based on UILPs. Promisingly, we are able to attribute CAAs based on their original texts with a weighted F1-score of 96.94%. Further, we are able to attribute CAAs according to their writing style (as specified by prompts), yielding a weighted F1-score of 95.84%, which sets the baseline for this task. By employing principal component analysis (PCA), we identify the top 100 most informative linguistic features for each CAA, achieving a weighted F1-score ranging from 86.04% to 97.93%, and an overall weighted F1-score of 93.86%.

## introduction recent advances in deep learning and natural language processing have led to the emergence of conversational ai agents (caas), hereby referred to as ai agents, which we define as large language models that can generate natural language as a dialogue system. these have been applied in tasks such as question answering @xcite , fake news detection and abuse detection @xcite . the widespread use of ai agents has highlighted the importance of determining the origin of a text @xcite @xcite @xcite @xcite and has led to a surge of interest in analysing the linguistic structures within them @xcite . one noteworthy linguistic aspect that remains unexplored is the determination of whether ai agents possess any uniquely identifiable linguistic patterns (uilps). our research draws inspiration from the linguistic theories of language identity and linguistic patterns within the compositions of individual authors @xcite . specifically, our study undertakes the task of assessing the validity of the aforementioned theories regarding ai agents. as a result, we have meticulously crafted the uilps for the following five generative large language models: gpt-4 foot_0 , gpt-3.5 1 , text-curie-001 1 , palm-2 foot_1 and llama2-7b foot_2 , aiming to ascertain the presence of uilps. the most effective method to confirm the usefulness of an identified uilp is through validation, a process achievable through the task of authorship attribution. authorship attribution for ai agents is the ability to ascertain the authorship of texts generated by ai agents @xcite @xcite . by establishing authorship, whether proving or disproving, we can reinforce the theory of distinct linguistic patterns in ai agents. we seek to answer crucial questions about the existence of uilps in ai agents, the linguistic overlap between various text types generated by these models, and the feasibility of identifying ai agents based on their individual uilps. the ability to prove the existence of uilps provides many benefits such as preventing the harmful use of ai agents (e.g., detecting fake news, hate speech, plagiarism). additionally, this enables the reuse of the uilp in classification tasks, potentially enhancing classification accuracy. we propose a transparent means for linguistic analysis that is more interpretable across different ai agents and forms the central emphasis of this paper. thus far, there has been no investigation on the uilp of ai agents, and there has been only limited comparison of different ai agents and little research indicating if these ai agents can be differentiated from each other based on their linguistic patterns. moreover, there is a notable absence of analysis of ai agents based on stylometry, i.e., the statistical analysis of language often used in the context of forensic linguistics @xcite . we propose both a feature-based machine learning classification as well as the use of transformer language models for ai agent classification. the research questions (rqs) we aim to answer in this paper are as follows: rq1: to what extent can we perform authorship attribution (aa) for ai agents based on their original texts, through the recognition of their uilps? rq2: can we attribute text to ai agents through the recognition of uilps in texts that they generated based on different stylistic prompts? rq3: how can we measure the linguistic overlap, if any, in outputs from the ai agent when it generates distinct texts? in addressing the above questions, we have made the following contributions: ## related work the analysis of authorship attribution encompasses two distinct categories: feature-based and large language model-based classification. feature-based approaches involve creating a specific feature set for a specific task @xcite . multivariate linguistic analysis paired with a traditional machine learning classifier is an example of this @xcite . approaches using pretrained transformer language modes have demonstrated superior accuracy with few preprocessing steps @xcite @xcite . these models significantly outperform traditional models in many cases. newer approaches use pre-trained transformer models and in some cases, these are combined with linguistic features @xcite . posited by @xcite , the principle of linguistic individuality states that at any given moment it is exceedingly improbable for two individuals to possess identical linguistic grammars. this principle is aligned with authorship attribution @xcite which assumes that writings from one author would exhibit greater linguistic similarity than writings from a different author @xcite . this theory has not been investigated in the case of ai agents, which is what we sought to achieve in our work. there has been a central focus on gpt models, with an emphasis on distinguishing between text written by humans and those generated by machines using transformer models @xcite @xcite @xcite , or surface-level linguistic features @xcite which have been regarded as a limited analysis when studied individually @xcite . other studies have utilised a primarily linguistic approach, analysing words and sentiment to distinguish human and machine-generated text @xcite . the limitations of previous approaches, compared to the methodology employed in this paper, become evident when considering their emphasis on distinguishing between human and machinegenerated content. these studies lack a comparative analysis of various ai agents and rarely incorporate multivariate stylometric analysis in their evaluation, which would better capture the use of ai agents in generating texts in other scenarios. @xcite investigated the attribution accuracies of synthetic text using transformer models (xlnet) and prior attribution approaches. other work has shown that traditional authorship attribution approaches cannot fully capture the style of an author when the author is a human (compared to when it is a machine). machine-generated authorship attribution is a comparatively straightforward task. machines do not display the same level of linguistic variety inherent in humans limiting their capacity to produce linguistically diverse texts @xcite . this limitation is imposed by their inability to evolve linguistically without being retrained @xcite . humans have a wide writing style which means their features and feature usage can differ depending on the text genre @xcite . this increases the complexity of authorship attribution tasks. ## conclusion and future work in our study, we have confirmed the presence of uniquely identifiable linguistic patterns (uilps) in conversational ai agents. this is supported by high accuracy in attribution for both original and stylistic data, with weighted f1-scores ranging from 93.88% to 96.96% when utilising the extended writeprint (ewp) and traditional machine learning-based classifiers. we also demonstrate similar performance when using a fine-tuned deberta model, achieving a 99.11% weighted f1-score. our results demonstrate that traditional machine learning-based models can obtain competitive attribution performance compared to a finetuned deberta model when utilising the ewp for classification. through pca analysis, we explored the attribution of ai agents based on their uilps. our results show that the combination of our ewp and rf classification effectively supports crossgenre attribution, with weighted f1-scores ranging from 94.17% to 97.50% for the stylistic data. this affirms the principle of linguistic individuality in ai agents, showcasing their uilps. these findings offer valuable insights into the distinctive linguistic patterns in text generated by ai agents, with potential applications in digital forensics, detecting fake news and plagiarism. future work will improve both the datasets introduced in this paper by expanding the size and scope of the stylistic prompts. we seek to perform a fine-grained linguistic analysis of a larger set of ai agents cross-lingually.