Evaluating and Aligning Human Economic Risk Preferences in LLMs
This addresses alignment of LLMs with human risk preferences for economic decision-making applications, though it appears incremental.
The study investigated whether LLMs align with human economic risk preferences across personas, finding they perform reasonably in simple personalized contexts but decline in complex tasks. The proposed alignment method improved LLM economic rationality in risk applications.
Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear. In this study, we investigate whether LLMs exhibit risk preferences consistent with human expectations across different personas. Specifically, we assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual's persona. Our results reveal that while LLMs make reasonable decisions in simplified, personalized risk contexts, their performance declines in more complex economic decision-making tasks. To address this, we propose an alignment method designed to enhance LLM adherence to persona-specific risk preferences. Our approach improves the economic rationality of LLMs in risk-related applications, offering a step toward more human-aligned AI decision-making.