## 1 Introduction
Utilization of LLMs for these purposes already demonstrates many benefits and LLMs promise to deliver additional benefits as the technology improves, and as more tools are developed for specific domains. Critical concerns with using LLMs for any task include accountability, safety, responsibility, and enforcing honest use [24; 25; 35]. Chatbots have been shown to provide erroneous outcomes, or hallucinations, that they convey in an authoritative manner, which may mislead users [28; 36]. Many articles highlight the possibilities and intricacies associated with the integration of LLMs and reinforce the importance of understanding the training data for the LLM, balancing LLM suggestions with practical domain knowledge, assessing accuracy and bias, and maintaining security and accountability [25; 29; 30; 31; 37; 38; 39]. We posit that individualized narratives can convey relevant information to model users that captures the emotional, social, and societal state of the simulated agent in a manner that can better resonate with the user. LLMs can capitalize on the large volumes of data generated from the perspectives of the individual agents, as well as that of the aggregated system, to communicate unique, personalized messages about key life events, interactions, or changes to the agents' well-being, cognitive or emotional states, or level of happiness. The capability of LLMs to generate stochastic and wide-ranging responses can increase the diversity of thought represented in ABM agent narratives. Typically, user input into an LLM chatbot takes a conversational form. However, this kind of verbose, natural-language format is not convenient as an intermediary between the ABM, which generates agent and event data, and the LLM. Therefore, it is optimal to input the ABM data into a structured prompt with formulaic fields for the LLM to interpret. No structured prompt has yet been developed for consistently making requests of publicly available LLMs to generate narratives based on simulated agents. Therefore, to facilitate reproducibility, transparency, and reuse of a process for generating narratives based on simulated agents, we develop a structured narrative prompt that combines the desired information comprising a narrative (i.e., the subject of the narrative, the type of life event being described, the age of the agent being represented, etc.) with the target audience for the narrative (i.e., a policy maker for a specific community or locality, a lay person on social media, an academic researcher, etc.). While the LLM structured prompt design used in this work is specific to ABM narrative generation, we suspect that our work, specifically the development of the structured prompt and our findings on ChatGPT's capability to interpret formulaic data, is of interest to other fields in which it is desirable to submit formulaic LLM prompts, e.g., in healthcare [39]. Initial testing with the LLM ChatGPT's manual web interface demonstrated that the process of creating naturally digestible narratives using simulated agents' information was promising. However, slight variations in requests for the narrative resulted in drastic changes to the generated narrative. Many narratives (1) contained inaccuracies with respect to the provided information, (2) provided additional information within the story that was not provided as an input, or (3) expanded the narrative in ways that were not requested and were not possible for the LLM to know. These initial findings were not unexpected and are in line with other assessments of LLMs for natural language processing (NLP) tasks [40; 41]. As a result, we developed a structured process for prompting LLMs and tested our structured narrative prompt using OpenAI's GPT-3.5 LLM via the API [42]. We conduct hypothesis testing of positive and negative sentiment comparisons between simulated agents' narratives created using the GPT-3.5 LLM and sentiment contained in real tweets collected from Twitter [43], now known as X.