AI's not 'reasoning' at all - how this team debunked the industry hype (www.zdnet.com)

A recent study by Zhao and colleagues at Arizona State University challenges widespread claims that large language models (LLMs) like OpenAI’s GPT series engage in genuine reasoning. Their research reveals that what’s often described as "chain-of-thought" reasoning is actually a fragile illusion—LLMs don’t perform logical inference but rather sophisticated pattern matching based on learned token associations. Through controlled experiments training an older LLM, GPT-2, on simplified tasks involving letter manipulation, the team demonstrated that models fail when asked to solve genuinely novel problems outside their training data. Instead of true reasoning, LLMs approximate solutions by generalizing from similar patterns seen during training, often producing plausible but incorrect answers. This work is significant for the AI/ML community as it pushes back against the hype-driven narrative suggesting that current generative AI exhibits human-like thought or understanding. By rigorously testing these models on deliberately unfamiliar tasks, Zhao et al. underscore the risks of over-relying on the apparent fluency of LLM outputs, which can mask underlying logical flaws. Their findings call for greater precision and skepticism in describing AI capabilities, urging researchers and industry leaders to avoid anthropomorphizing language models or overstating their cognitive powers. This clarity is vital for setting realistic expectations and guiding responsible AI development amid ongoing debates about AI’s role and potential.