LLMs still can't reason like humans

Recent discussions highlight the limitations of large language models (LLMs) in reasoning compared to humans. A simple experiment involving a plate of vegetables illustrates these shortcomings. When asked how many vegetables remain on a plate after being flipped upside down, leading LLMs like GPT-4o and Claude 3.5 Sonnet often provide incorrect answers, typically selecting options that suggest some vegetables remain. This failure points to a broader issue of spatial reasoning, where LLMs struggle to understand basic cause-and-effect scenarios. A new project called "Simple Bench" aims to quantify these failures by evaluating LLM responses to a series of carefully crafted questions. Initial results show that while humans score around 92%, the best-performing LLMs only achieve about 27%. The core issue lies in the nature of LLMs; they are designed to model language rather than reality, focusing on predicting the next word rather than understanding physical interactions. This limitation suggests that simply scaling up LLMs may not lead to significant improvements in reasoning capabilities. The findings from Simple Bench could inform future AI development, helping to identify and address common reasoning failures in LLMs.

- LLMs struggle with basic reasoning tasks, often providing incorrect answers to simple questions.

- A new benchmarking project, "Simple Bench," quantifies LLM performance in reasoning.

- Humans outperform LLMs significantly in reasoning tasks, highlighting a substantial gap.

- LLMs are designed to model language, not reality, which contributes to their reasoning failures.

- Future AI development may benefit from insights gained through identifying LLM shortcomings.