Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLM s (2024)

Abstract: Recent advances in large language models (LLM) have enabled richer social simulations, allowing for the study of various social phenomena. However, most recent work has used a more omniscient perspective on these simulations (e.g., single LLM to generate all interlocutors), which is fundamentally at odds with the non-omniscient, information asymmetric interactions that involve humans and AI agents in the real world. To examine these differences, we develop an evaluation framework to simulate social interactions with LLMs in various settings (omniscient, non-omniscient). Our experiments show that LLMs perform better in unrealistic, omniscient simulation settings but struggle in ones that more accurately reflect real-world conditions with information asymmetry. Moreover, we illustrate the limitations inherent in learning from omniscient simulations. Our findings indicate that addressing information asymmetry remains a fundamental challenge for LLM-based agents.

## introduction llm-based social simulations have become great tools for modeling human behavior in various contexts @xcite , understanding and measuring llms' social skills through certain tasks @xcite , improving and aligning chatbot systems by providing training data @xcite @xcite . the success in replicating human-like social interactions across diverse domains offers a promising picture of the realistic social capabilities of llms. however, the role of information asymmetry in these simulations, i.e., the degree to which interlocutors in interactions have access to each other's internal private mental states and goals, has been largely overlooked @xcite ; matrix @xcite each llm agent embodies a character and maintains private information one llm generates all interactions of both sides at once sotopia @xcite figure 1 : an illustration between script mode simulation and agents mode simulation. in the agents mode, two agents, each equipped with an llm, negotiate and strategically seek information to reach a mutual agreement. conversely, in script mode, a single omniscient llm orchestrates the entire interaction based on full access to the agents' goals. these two modes end up on opposite sides of the spectrum in terms of information asymmetry from various perspectives (e.g., roles, social goals, secrets, etc.). @xcite foot_0 . instead of using the more realistic simulation setting that mirrors human daily social interactions with information asymmetry (e.g., agents mode in figure 1 ), a wide range of prior research has leveraged a more omniscient perspective to model and simulate social interactions with llms @xcite @xcite . by generating all sides of interaction at once or making agent social goals or tasks transparent to all participants, these simulations diverge from the non-omniscient human interactions that rely on social inference to achieve goals in real-world scenarios @xcite . studying these omniscient simulations could lead to biased or wrong conclusions about llms' social capabilities @xcite . to investigate the effect of this incongruity, we create a unified simulation framework with two distinct modes for simulating human interaction with llms: script mode and agents mode. as shown in figure 1 , in the script mode, one omniscient llm has access to all the information and generates the entire dialogue from a third-person perspective (e.g., @xcite ). in the agents mode, two llm agents assume distinct roles and engage in interaction to accomplish the task (e.g., @xcite . these modes represent the opposite ends of the spectrum regarding information asymmetry, while the agents mode is the realistic interaction simulation setting that reflects the information asymmetry in human daily-life interactions. we first compare the interactions produced in these two simulation modes, examining the extent to which the simulated characters achieve their social goals at the end of the interaction, as well as the naturalness of the interactions. we find that llms in the agents mode not only struggle to generate social interactions that effectively meet the specified social goals for each role but also produce less naturally flowing social interactions, particularly in their utterances when compared to the llms in the script mode. these findings indicate that llms still fall short of acting as agents and simulating social interaction within contexts of realistic human interaction settings. we then ask the question of whether llm agents can be learned from script simulations. inspired by @xcite ; @xcite , we finetune gpt-3.5 @xcite on a large dataset of interactions generated in the script mode. we find that finetuning on omnisciently generated social interactions provides limited improve for llms interacting in the agents mode. further data analysis reveals the biases within script mode simulations, hindering the ability of models trained on such data to effectively generalize real-world social skills. based on our findings, we provide recommendations for reporting llm-based agent work, encouraging more careful considerations and transparency in using llms to simulate social interactions from both data and learning perspectives. ## background & related work agent-based modeling and social simulations have a long history in social sciences for specific tasks (e.g., decision making, business, cognitive science, etc.). more recently, advances in llms have sparked a new wave of simulations tackling more open-ended and complex social scenarios. we review some recent progress in these directions below and highlight different themes and shortcomings of these prior methods. ## script vs agents simulation to investigate whether the success of the omniscient script mode reflects how llms would behave in the realistic human communication setting, we set up a unified framework to generate synthetic text data for different simulation settings and compare the performance of llms in these settings. in this section, we first introduce the general framework of agent-based simulation and script simulation, and then we simulate social interactions across these settings to answer the following research questions (rq): rq1: do the script simulations reflect how llms achieve social goals in the realistic soical interaction settings? rq2: do the script simulations reflect how llms communicate in the realistic soical interaction settings? ## learning from generated stories given that the script mode produces more "successful" and natural social interactions, this raises the question of whether models can improve their social skills in the more realistic setting (i.e., agents mode) by learning from the generated scripts @xcite . we finetune gpt-3.5 on the simulations of script to answer: rq3: can a specialized llm finetuned on the script simulations reach the same level of success (goal completion and naturalness) as the script simulations in the agent mode? rq4: if not, what are the potential aspects of script simulations that hinder the llms as agents from learning social skills? ## conclusion & discussion we scrutinize recent advances in social simulation by evaluating current approaches' ability to generalize to settings that are closer to human interaction. focusing on cooperation and competition given information-asymmetric settings, we evaluate three modes of deploying llms based on past approaches in the literature. we find that llms continue to face challenges when operating in more realistic agents mode. meanwhile, the simulations generated from the script mode show biases toward exploiting white box access to the participants early in the interaction. furthermore, we find that finetuning models on these generations improve selectively on a measure of goal completion from sotopia, but it also imbues the implausible strategies from the 'omniscient' script simulations into the student models, resulting in further bias.