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UC San Diego, Tsinghua University researchers just made AI way better at knowing when to ask for help
UC San Diego and Tsinghua University researchers develop breakthrough AI method that teaches small language models when to use tools versus internal knowledge, achieving 28% better accuracy while using fewer resources than larger models like GPT-4.
A team of computer scientists has developed a method that helps artificial intelligence understand when to use tools versus relying on built-in knowledge, mimicking how human experts solve complex problems. This trend toward “AI downsizing” reflects growing recognition that bigger isn’t always better — specialized, efficient models can often match or exceed the performance of their larger counterparts while using far fewer computational resources. Moreover, this development suggests a future where AI systems could be more cost-effective and reliable partners in scientific work, capable of making nuanced decisions about when to leverage external resources — much like a seasoned professional who knows exactly when to consult specialized tools versus rely on their expertise.
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