Google DeepMind proposes ‘self-discover’ framework for LLMs, improves GPT-4 performance

Key Points:

  • Self-discover framework improves performance on challenging reasoning benchmarks
  • Models look at atomic reasoning modules and compose explicit reasoning structure
  • Self-discover approach is more efficient and outperforms chain-of-thought reasoning

Summary:

Google DeepMind and the University of Southern California have unveiled an innovative ‘self-discover’ prompting framework to enhance reasoning capabilities. This morning’s publication on arXiV and Hugging Face introduces a revolutionary approach that surpasses existing techniques, significantly boosting the performance of esteemed models like OpenAI’s GPT-4 and Google’s PaLM 2.

 

Dubbed ‘self-discover,’ this cutting-edge framework delves into task-intrinsic reasoning structures, enabling LLMs to decode complex problems with exceptional precision. By integrating multiple atomic reasoning modules like critical thinking and step-by-step logic, the models craft explicit reasoning structures for seamless decoding. This method accomplishes stellar results with significantly less inference compute, a game-changer for enterprises seeking efficiency.

 

While traditional prompting techniques have been effective, they often rely on implicit assumptions to tackle tasks, limiting their adaptability to unique problem structures. The self-discover framework excels by autonomously discerning the inherent structure of tasks, selecting the most suitable reasoning technique, and optimizing efficiency simultaneously.

 

In rigorous testing across various models and tasks, self-discover outshone prevailing methods, showcasing performance gains of up to 32% while reducing the need for compute resources by 10 to 40 times. With remarkable accuracies on challenging benchmarks such as Big-Bench Hard and Math tasks, self-discover proves to be a potent tool for elevating problem-solving capabilities and inching closer to achieving general intelligence.

 

Moreover, the transferability studies hint at the broad applicability of the reasoning structures across model families, underscoring the promising prospects for Human-AI collaboration. As this promising new approach takes its first steps towards revolutionizing problem-solving, the future holds exciting possibilities for advancing LLM structured reasoning and unlocking the synergies between human and artificial intelligence realms.”

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