SELF-DISCOVER Framework

A SELF-DISCOVER Framework is a LLM Framework that enhances the capabilities of LLM-based Systems by enabling them to autonomously construct tailored reasoning structures.

• Context:
  • It can autonomously select and assemble necessary reasoning modules, such as Critical Thinking and Step-by-Step Analysis, to form task-specific reasoning structures.
  • It can introduce a Self-Discovery Process that marks a significant departure from traditional methods, enhancing the LLM's problem-solving capabilities.
  • It can demonstrate remarkable performance enhancements on challenging reasoning benchmarks, showing significant improvements over existing methods with less computational demand.
  • It can be universally applicable across various LLM families, showcasing the framework's adaptability.
  • It can offer more interpretable and explicit insights into how LLMs understand and solve tasks, unlike conventional methods that rely on optimized prompts.
  • It can employs a two-stage process for the discovery and application of reasoning structures, ensuring efficiency and effectiveness in problem-solving.
  • It can outperform other inference-heavy methods by requiring fewer computational resources for similar or enhanced performance outcomes.
  • It can align LLM reasoning patterns more closely with human problem-solving approaches, highlighting potential avenues for improved human-AI collaboration.
  • ...
• Example(s):
  • SELF-DISCOVER, v1.
  • ...
• Counter-Example(s):
  • Socratic AI: A framework from Princeton NLP Group employing three LLM-based agents to engage in Socratic dialogues for collaborative problem-solving and knowledge discovery.
  • LeMUR: Developed by AssemblyAI, this framework leverages LLMs for interpreting and summarizing spoken data, integrating with advanced speech recognition for high-quality transcription.
  • DeLLMa: A decision-making assistant framework that enhances decision accuracy under uncertainty by using a multi-step approach based on
• See: LLM-based System, Critical Thinking, Step-by-Step Analysis, Human-AI Collaboration.

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References

2024

• (Zhou, Pujara et al., 2024) ⇒ Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, and Huaixiu Steven Zheng. (2024). “Self-Discover: Large Language Models Self-Compose Reasoning Structures.”
  • NOTES:
    • It introduces a self-discovery process where LLMs autonomously select and integrate various atomic reasoning modules (e.g., critical thinking, step-by-step analysis) into a coherent reasoning structure tailored to the specific task at hand.
    • It can demonstrate performance improvements on challenging reasoning benchmarks (e.g., BigBench-Hard, MATH) with up to 32% enhancement over existing methods like Chain of Thought (CoT), and over 20% against CoT-Self-Consistency while requiring significantly less computational resources.
    • It can suggest a universal applicability of the self-discovered reasoning structures across different LLM families (e.g., from PaLM 2-L to GPT-4), highlighting the adaptability of the framework.
    • It can provide a more interpretable insight into task-solving, as the reasoning structures are explicit and reflect LLM's understanding of the task, compared to traditional optimized prompts.
    • It can employ a two-stage process for reasoning structure discovery and application, where the first stage identifies a task-specific structure, and the second stage applies this structure to solve individual task instances.
    • It can showcase the efficiency of SELF-DISCOVER, which outperforms other inference-heavy methods by requiring significantly fewer computational resources for similar or better performance.
    • It can align LLM reasoning patterns with human reasoning, as shown by the commonalities between self-discovered reasoning structures and human problem-solving approaches, suggesting the potential for enhancing human-AI collaboration.

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