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.

Large pre-trained language models are acknowledged to carry social bias towards different demographics, which can further amplify existing stereotypes in our society and cause even more harm. Text-to-SQL is an important task, models of which are mainly adopted by administrative industries, where unfair decisions may lead to catastrophic consequences. However, existing Text-to-SQL models are trained on clean, neutral datasets, such as Spider and WikiSQL. This, to some extent, cover up social bias in models under ideal conditions, which nevertheless may emerge in real application scenarios. In this work, we aim to uncover and mitigate social bias in Text-to-SQL models. We summarize the categories of social bias that may occur in structural data for Text-to-SQL models. We build test benchmarks and reveal that models with similar task accuracy can contain social bias at very different rates. We show how to take advantage of our methodology to assess and mitigate social bias in the downstream Text-to-SQL task.