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A recent MIT study reveals that large language models struggle with unfamiliar problems and make human-like reasoning mistakes, as researchers find. These models are primarily information embedding and next token prediction systems, not reasoning systems. Despite being useful tools, many apply the wrong mental model to them, leading to overestimation of their reasoning skills.
Reasoning skills of large language models are often overestimated, researchers find https://t.co/f92T4UuIdE #skills #language #models #researchers https://t.co/kkxz0JtNZe
Large language models make human-like reasoning mistakes, researchers find | TechXplore Large language models (LLMs) can complete abstract reasoning tasks, but they are susceptible to many of the same types of mistakes made by humans. Andrew Lampinen, Ishita Dasgupta, and… https://t.co/9SYSG3GncH
Large language models make human-like reasoning mistakes, researchers find https://t.co/M7ZaRkBygg #humans #llms #content #relationships #rule #authors #tasks #task #wason #selection