In this research project, we will explore the potential of large language models (LLMs) to generate semantic representations s to replace human ratings and to model the language comprehension of specific individuals. Estimates of semantic features generated by LLMs will be compared to human
benchmarks. Prompt engineering will be used to investigate whether LLMs can simulate individual differences in language comprehension.