Unraveling the Enigma of SPLIT in Large-Language Models: The Unforeseen Impact of System Prompts on LLMs with Dissociative Identity Disorder

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 [1]. The advent of large-language models has raised intriguing questions about the presence of such conditions in LLMs [2]. 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.