Large language models (LLMs) are becoming increasingly useful for programming and robotics tasks, but for more complicated reasoning problems, the gap between these systems and humans looms large. Without the ability to learn new concepts like humans do, these systems fail to form good abstractions — essentially, high-level representations of complex concepts that skip less-important details — and thus sputter when asked to do more sophisticated tasks.Luckily, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have found a treasure trove of abstractions within natural language. In three papers to…
