Apple's recent research paper scrutinizes the widely held belief that large language models (LLMs) and their derivatives can reason reliably. The study uncovers substantial flaws in models like ChatGPT and Claude, particularly when faced with complex problems that deviate from their training data. This aligns with historical critiques of LLMs, including Josh Wolfe's supportive remarks about their limitations. Consequently, AI advocates are acknowledging these challenges while still optimistic about advancements in model capabilities. The paper showcases a need for a thorough understanding of AI limitations and sparks debate in the tech community regarding future directions for neural networks.
Josh Wolfe's comments on X highlight the challenges facing large language models (LLMs), emphasizing their limitations in reasoning and general intelligence as exposed by Apple's recent research.
Apple's research paper argues that despite their impressive appearance, leading models like ChatGPT struggle with complex reasoning tasks, often collapsing under pressure.
The findings suggest that LLMs excel in familiar patterns but fail when faced with novel situations, confirming long-standing arguments about their training limitations.
The paper catalyzes a reassessment in the AI community, with advocates conceding to the research's insights while still maintaining hope for future developments.
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