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Recent research highlights the potential for training Large Language Models (LLMs) on elementary cellular automata (ECA) to enhance their intelligence. The study, titled 'Intelligence at the Edge of Chaos,' suggests that LLMs exhibit optimal performance when exposed to systems with a high but not excessive level of complexity, referred to as the 'edge of chaos.' This approach indicates that intelligence in LLMs may arise from balancing predictability and complexity. The findings also reveal that while LLMs can mimic human reasoning patterns, they often struggle with more complex tasks that humans solve easily. Additionally, the study notes that LLMs display a mix of noisy reasoning, memorization, and probability-based prediction, even under chain-of-thought prompting. Researchers from Yale, NSULA, and IdahoStateU found that this balance could improve performance by up to 20%.
Apple AI paper finds a lot of fragility in mathematical reasoning of LLMs (performance varies a lot w different instantiations of same question); authors hypothesise that current LLMS not capable of genuine logical reasoning. Interested in discussion. https://t.co/ZSm9aKCsUb
Intelligence in LLMs arises at the "edge of chaos," balancing order and randomness ECAs and LLMs in this zone adapt better to tasks, improving performance by up to 20% Striking the balance between control and flexibility may be a key to building more creative and adaptive AI https://t.co/5RojLXwHgI
Has mathematical reasoning in LLMs really advanced? This study tests several SoTA models on a benchmark created with symbolic templates that enable diverse mathematical problems. They find that LLMs exhibit variance when responding to variations of the same questions. The… https://t.co/cLjUKZp90q
