@inproceedings{
zhou2024selfdiscover,
title={{SELF}-{DISCOVER}: Large Language Models Self-Compose Reasoning Structures},
author={Pei Zhou and Jay Pujara and Xiang Ren and Xinyun Chen and Heng-Tze Cheng and Quoc V Le and Ed H. Chi and Denny Zhou and Swaroop Mishra and Steven Zheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=BROvXhmzYK}
}
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.
SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures
Pei Zhou Jay Pujara Xiang Ren Xinyun Chen Heng-Tze Cheng Quoc V Le Ed H. Chi Denny Zhou Swaroop Mishra Steven Zheng