Self-Discover: Large Language Models Self-Compose Reasoning Structures
<|reference_start|>Self-Discover: Large Language Models Self-Compose Reasoning Structures: We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods. Core to the framework is a self-discovery process where LLMs select multiple atomic reasoning modules such as critical thinking and step-by-step thinking, and compose them into an explicit reasoning structure for LLMs to follow during decoding. SELF-DISCOVER substantially improves GPT-4 and PaLM 2's performance on challenging reasoning benchmarks such as BigBench-Hard, grounded agent reasoning, and MATH, by as much as 32% compared to Chain of Thought (CoT). Furthermore, SELF-DISCOVER outperforms inference-intensive methods such as CoT-Self-Consistency by more than 20%, while requiring 10-40x fewer inference compute. Finally, we show that the self-discovered reasoning structures are universally applicable across model families: from PaLM 2-L to GPT-4, and from GPT-4 to Llama2, and share commonalities with human reasoning patterns.<|reference_end|>
@article{zhou2024self-discover:,
    title={Self-Discover: Large Language Models Self-Compose Reasoning Structures},
    author={Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V.
  Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, Huaixiu Steven Zheng},
    journal={arXiv preprint arXiv:2402.03620},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03620},
    primaryClass={cs.AI cs.CL}
}