Enhancing Reasoning Abilities of Language Models

Researchers from Google DeepMind and the University of Southern California have introduced a groundbreaking approach to improve the reasoning abilities of large language models (LLMs).

SELF-DISCOVER Prompting Framework

Their new ‘SELF-DISCOVER’ prompting framework, published on arXiV and Hugging Face, aims to significantly enhance the performance of leading models such as OpenAI’s GPT-4 and Google’s PaLM 2.

• Performance Increase: The framework promises a substantial performance increase, with up to 32% improvement compared to traditional methods like Chain of Thought (CoT).
• Reasoning Structures: LLMs autonomously uncover task-intrinsic reasoning structures to navigate complex problems.
• Utilization of Reasoning Modules: The framework empowers LLMs to self-discover and utilize various atomic reasoning modules to construct explicit reasoning structures.

Framework Operation

The framework operates in two stages:

• Stage One: Composing a coherent reasoning structure intrinsic to the task, leveraging a set of atomic reasoning modules and task examples.
• Decoding: LLMs follow the self-discovered structure to arrive at the final solution.

Performance and Implications

In extensive testing across various reasoning tasks, the self-discover approach consistently outperformed traditional methods. Additionally, the framework paves the way for tackling more challenging problems and brings AI closer to achieving general intelligence.

Conclusion

Breakthroughs like the SELF-DISCOVER prompting framework represent crucial milestones in advancing the capabilities of language models. This research offers a glimpse into the future of AI and its potential to revolutionize various industries.