LLMs’ “simulated reasoning” abilities are a “brittle mirage,” researchers find

The study, conducted by researchers at the University of Chicago and OpenAI, examined the "simulated reasoning" abilities of large language models (LLMs) such as GPT-3. The findings suggest that these models' apparent reasoning capabilities are a "brittle mirage" that "degrades significantly" when asked to generalize beyond their training data. The researchers found that LLMs perform well on tasks that closely match their training, but their performance declines sharply when faced with even minor variations or more complex reasoning challenges. This raises concerns about the true capabilities of these AI systems and their ability to engage in genuine, transferable reasoning. The study highlights the need for further research and development to improve the robustness and generalization abilities of LLMs, as well as the importance of critically examining the limitations and biases inherent in these systems. The findings serve as a cautionary tale for the hype and enthusiasm surrounding the current state of AI technology.