We analyze GPT-4o’s ability to represent numeric information in texts for elementary school children and assess it with respect to the human baseline. We show that both humans and GPT-4o reduce the amount of numeric information when adapting informational texts for children but GPT-4o retains more complex numeric types than humans do.

We evaluate linguistic proficiency of humans and LLMs on pronoun resolution in Japanese, using the Winograd Schema Challenge dataset. Humans outperform LLMs in the baseline condition, but we find evidence for task demand effectss in both humans and LLMs. We also found that LLMs surpass human performance in scenarios referencing US culture, providing strong evidence for content effects.