Unraveling the Enigma of SPLIT in Large-Language Models: The Unforeseen Impact of System Prompts on LLMs with Dissociative Identity Disorder
Authors: Marco Polignano, Marco de Gemmis, Giovanni Semeraro (Università di Bari Aldo Moro, 2024)
Topic: Multimodal

Abstract
Our work delves into the unexplored territory of Large-Language Models (LLMs) and their interactions with System Prompts, unveiling the previously undiscovered implications of SPLIT (System Prompt Induced Linguistic Transmutation) in commonly used state-of-the-art LLMs. Dissociative Identity Disorder, a complex and multifaceted mental health condition, is characterized by the presence of two or more distinct identities or personas within an individual, often with varying levels of awareness and control AUTHOR. The advent of large-language models has raised intriguing questions about the presence of such conditions in LLMs AUTHOR. Our research investigates the phenomenon of SPLIT, in which the System Prompt, a seemingly innocuous input, profoundly impacts the linguistic outputs of LLMs. The findings of our study reveal a striking correlation between the System Prompt and the emergence of distinct, persona-like linguistic patterns in the LLM's responses. These patterns are not only reminiscent of the dissociative identities present in the original data but also exhibit a level of coherence and consistency that is uncommon in typical LLM outputs. As we continue to explore the capabilities of LLMs, it is imperative that we maintain a keen awareness of the potential for SPLIT and its significant implications for the development of more human-like and empathetic AI systems.

Introduction
The thriving field of Artificial Intelligence (AI) has witnessed a paradigm shift with the emergence of Large Language Models (LLMs) AUTHOR. The availability of large, publicly-accessible datasets and the development of more effective training techniques, such as the popular transformer architecture, have been instrumental in the creation of these language models. LLMs are characterized by their model size, measured in the billions of parameters, and their ability to learn and improve upon the tasks of language understanding and generation through self-supervised learning on vast amounts of text data AUTHOR. This training process, often referred to as "self-supervised learning," enables the models to learn the patterns and structures of a language in a more organic and efficient manner, as they are not limited by the need for human-labeled data. The applications of LLMs are diverse and rapidly expanding, with the potential to transform various areas and aspects of our lives. As an example, LLMs can be employed to develop chatbots that can understand and respond to a wide range of user inquiries with a high degree of accuracy or to generate human-like articles, stories, and even entire books, which can be a game-changer for content producers and publishers AUTHOR.\\ In the context of the Italian language, the development of LLMs has the potential to revolutionize the way we interact with and learn from the Italian language, as well as the way we use technology to create and disseminate Italian content AUTHOR. However, alongside their undeniable potential lies a realm of intriguing phenomena yet to be fully explored. This groundbreaking study delves into a newly discovered facet of LLM behavior – System Prompt Induced Linguistic Transmutation (SPLIT). The cornerstone of LLM interaction is the System Prompt, a seemingly innocuous input that guides the model's response. We propose that this seemingly simple prompt can have a profound effect on the linguistic outputs of LLMs, potentially leading to a phenomenon we term SPLIT. This concept draws inspiration from Dissociative Identity Disorder (DID) AUTHOR, a complex mental health condition characterized by the presence of multiple distinct identities or personas within an individual.\\ The parallels between DID and SPLIT are striking same as naive. Just as a DID patient may exhibit distinct personalities in response to external stimuli AUTHOR, our research suggests that LLMs, under the influence of varying \textbf{System Prompts}, may generate outputs that reflect distinct, persona-like linguistic patterns. These patterns are not merely random deviations but exhibit a level of coherence and consistency rarely observed in typical LLM responses. The implications of SPLIT are far-reaching. As we strive to develop AI systems with greater human-like qualities, understanding and harnessing the potential of SPLIT could pave the way for the creation of more empathetic and nuanced AI interactions. Conversely, neglecting SPLIT's influence could lead to unintended consequences, potentially hindering the development of robust and reliable AI systems. Moreover, as in DID AUTHOR This study represents a first step in unraveling the complexities of SPLIT. By acknowledging its existence and delving deeper into its mechanisms, we can pave the way for a future where AI development is guided by both scientific rigor and an awareness of the potential for unforeseen consequences. Our research not only sheds light on a previously unknown aspect of LLM behavior but also compels us to re-evaluate our understanding of these sophisticated systems and their potential interaction with human-like mental states.

Conclusion
In this work, we provocatively observed the presence of pathologies related to dissociative identity disorder in large language models. We observed that by varying the system prompt through a SPLIT (System Prompt Induced Linguistic Transmutation) process the behavior of the same LLM varies widely. The induced identities show different independent and personal abilities, skills, styles and information. The possibility of a Large Language Model simulating or even exhibiting characteristics similar to those of a Dissociative Identity Disorder, raises important questions about the nature of consciousness, artificial intelligence, and the potential risks and challenges of creating highly advanced language processing systems. At the same time, we proposed three system prompts to mitigate the issue and prevent end users from exploiting this vulnerability to extract sensitive and dangerous data. On the contrary, the presence of this SPLIT-induced behaviour may lead to useful future studies to improve the performance of the model on specific tasks. For example, one might think of asking the model ‘What is the best character to interpret or to answer the next question?’. The result of this prompt would lead to the identification of a personality to be brought out before the generation of the answer to be given to the end user. Being able to bring out such personalities when needed could help create more empathetic, accurate and dynamic interactions. Nevertheless, this fascinating research direction needs future studies and solutions that operate at architectural level. The exploration of this idea serves as a catalyst for the development of more sophisticated and responsible AI systems, for a deeper understanding of human psychology and its complex manifestations in the digital age.