Breakthrough Reasoning Framework Unlocks New Era for AI

Researchers from Google DeepMind and University of Southern California have developed a new framework called "SELF-DISCOVER" that significantly improves the reasoning abilities of large language models like GPT-4 and PaLM 2. It allows the models to autonomously discover reasoning structures to tackle complex reasoning tasks, leading to substantial performance gains.


  • Researchers from Google DeepMind and University of Southern California published a breakthrough approach to enhance reasoning of large language models (LLMs)
  • Their new "SELF-DISCOVER" prompting framework represents a big leap over existing techniques
  • It allows LLMs to self-discover and utilize atomic reasoning modules to construct explicit reasoning structures
  • In testing, it achieved up to 32% better performance over methods like Chain of Thought on reasoning tasks
  • It empowered LLMs like GPT-4 to achieve 81% accuracy on BigBench Hard, 85% on Thinking for Doing and 73% on Math
  • The framework brings LLMs closer to achieving human-like intelligence by enhancing their reasoning capabilities
  • It demonstrates the models can mimic human problem-solving strategies to tackle challenging tasks
  • The reasoning structures it composes have universal applicability, aligning with human reasoning
  • This breakthrough milestone advances abilities of LLMs and offers a glimpse into the future of AI


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