Experiments reveal LLMs develop their own understanding of reality as their language abilities improve

Ask a large language model (LLM) like GPT-4 to smell a rain-soaked campsite, and it’ll politely decline. Ask the same system to describe that scent to you, and it’ll wax poetic about “an air thick with anticipation” and “a scent that is both fresh and earthy,” despite having neither prior experience with rain nor a nose to help it make such observations. One possible explanation for this phenomenon is that the LLM is simply mimicking the text present in its vast training data, rather than working with any real understanding of rain or smell.