Experimental Evidence on Large Language Models

This paper investigate the formation of inflation expectations using Large Language Models (LLMs) based on different text data. Employing a new experimental design, I integrate generative AI with economic analysis to explore the impact of different information treatments on LLMs' responses. Results from six distinct knowledge sources reveal that the type of information accessible to an LLM significantly affects the variance of its generated expectations. LLMs with access to relevant economic documents exhibit lower variance compared to those with irrelevant information. Furthermore, information treatments, particularly the one related to mortgage rates, influence the updating of LLMs' prior inflation expectations, showing similar findings from human surveys. The findings underscore the importance of providing domain-specific knowledge to LLMs and showcase the potential of AI agents in studying expectation formation and decision-making processes in economics.