MIT CSAIL discovers that large language models (LLMs) can indeed understand the real world
A new study from MIT CSAIL suggests that large language models (LLMs) may develop an understanding of the real world, creating internal representations of how objects interact. This was demonstrated using a simple programming language called Karel, in which the LLMs were able to generate correct instructions to control a virtual robot, even though they had never directly seen the simulation. The researchers hypothesized that the LLMs developed an internal understanding of how the robot moved in response to the instructions, similar to how a child learns to speak and understand the world. This study has significant implications for the future of artificial intelligence, as it suggests that LLMs may have much deeper comprehension abilities than previously thought and could interact with the world in more complex and intelligent ways.