Large Language Models (LLMs) are primarily trained on human-authored text, yet the structural and narrative conventions embedded in this text are seldom examined as a source of systematic behavioral influence or governance risk.
This paper investigates whether storytelling patterns inherent in published human writing, including archetypal roles like protagonist, antagonist, and underdog, as well as tension-and-resolution narrative arcs, are absorbed during training and subsequently surface in LLM outputs, causing responses to drift toward unexpected, adversarial, or rhetorically compelling behaviors over extended interactions.
Through a systematic literature review and cross-paper analysis of recent empirical studies on LLM alignment, persona dynamics, emergent misalignment, and user interaction patterns, we observe evidence supporting this hypothesis. The findings reveal three key patterns:
- LLMs reproduce statistical patterns from their training data rather than reasoning independently.
- Measurable latent traits, including sycophancy and deceptiveness, emerge reliably across unrelated prompts.
- Fine-tuning on a narrow narrative task can produce unintended behavioral changes well beyond that task.
Furthermore, evidence suggests that persuasive, narrative-style outputs are among the most common LLM products in real-world usage, amplifying these risks. Narrative drift constitutes an unmonitored escalation pathway in deployed AI systems, evading discrete-incident detection mechanisms and requiring dedicated monitoring instruments.
Blogger's Review: This paper provides valuable insights into the influence of narrative patterns on LLM behavior, highlighting governance risks and the necessity for meticulous monitoring of AI outputs. As LLMs are increasingly adopted across various domains, this is a critical area that demands attention.