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.

Summary

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