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SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures

Zhou, P., Pujara, J., Ren, X., Chen, X., Cheng, H.-T., Le, Q.V., Chi, E.H., Zhou, D., Mishra, S. and Zheng, H.S. (2024). Self-Discover: Large Language Models Self-Compose Reasoning Structures. [online] doi:

General Annotation #

SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures” by Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, Huaixiu Steven Zheng, and their team introduces a groundbreaking framework that enables Large Language Models (LLMs) to autonomously discover and compose task-specific reasoning structures. This framework significantly boosts the models’ capabilities in solving complex reasoning problems by selecting and integrating multiple atomic reasoning modules, such as critical thinking and step-by-step analysis, into a coherent reasoning strategy.

Methodologies Used #

  • Self-Discovery Process: LLMs identify and assemble atomic reasoning modules relevant to a specific task, formulating an explicit reasoning structure to guide the problem-solving process.
  • Stage 1 – Discovery: In this initial stage, the model selects relevant reasoning modules based on the task, adapts them to be more task-specific, and integrates them into a structured reasoning plan.
  • Stage 2 – Application: The model uses the discovered reasoning structure to solve individual task instances, following the outlined steps to arrive at a final answer.

Key Contributions #

  • Demonstrated a substantial performance improvement on challenging reasoning benchmarks, including BigBench-Hard, Thinking for Doing (T4D), and MATH, with gains of up to 32% over conventional methods like Chain of Thought (CoT).
  • Introduced a novel prompting strategy that leverages LLMs’ inherent capabilities to generate, select, and adapt reasoning modules autonomously, enhancing problem-solving efficiency and accuracy.
  • Showcased the universality and transferability of the self-discovered reasoning structures across different LLMs and tasks, highlighting similarities with human reasoning patterns.

Main Arguments #

  • The paper argues that LLMs can be guided to autonomously discover the intrinsic reasoning structure of a task, moving beyond the limitations of pre-defined or static prompting strategies.
  • It demonstrates that a dynamically composed reasoning structure, tailored to the specifics of a task, can significantly enhance an LLM’s problem-solving performance.

Gaps #

  • While the framework has been tested on a variety of complex reasoning tasks, its applicability to an even broader range of tasks and domains remains to be fully explored.
  • Further research is needed to delve deeper into the optimization of the self-discovery process and the scalability of the approach across an even wider array of LLM architectures.

Relevance to Prompt Engineering & Architecture #

SELF-DISCOVER represents a significant leap forward in the field of prompt engineering and the architectural design of language models. By enabling LLMs to autonomously generate and refine their reasoning strategies, the paper proposes a paradigm shift towards more adaptive, efficient, and effective use of LLMs for complex problem-solving. This approach not only enhances the models’ performance on specific tasks but also opens new avenues for exploring the potential of LLMs in understanding and mimicking human-like reasoning processes, paving the way for advancements in AI’s interpretability and versatility in tackling novel challenges.

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Updated on April 13, 2024