The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
Jingyuan Qi, Zhiyang Xu, Ying Shen, Minqian Liu, Di Jin, Qifan Wang,, Lifu Huang

TL;DR
This paper introduces SOCRATIC QUESTIONING, a recursive reasoning algorithm for large language models that improves complex problem-solving by mimicking human recursive thinking, leading to better accuracy than existing methods.
Contribution
It proposes a novel divide-and-conquer recursive algorithm for LLM reasoning, inspired by human cognition, outperforming prior chain-of-thought prompting techniques.
Findings
Significant performance improvements on MMLU, MATH, LogiQA, and visual QA tasks.
Intermediate reasoning steps resemble human recursive thinking.
More robust to errors compared to chain-of-thought prompting.
Abstract
Chain-of-Thought (CoT) prompting enables large language models to solve complex reasoning problems by generating intermediate steps. However, confined by its inherent single-pass and sequential generation process, CoT heavily relies on the initial decisions, causing errors in early steps to accumulate and impact the final answers. In contrast, humans adopt recursive thinking when tackling complex reasoning problems, i.e., iteratively breaking the original problem into approachable sub-problems and aggregating their answers to resolve the original one. Inspired by the human cognitive process, we propose SOCRATIC QUESTIONING, a divide-and-conquer style algorithm that mimics the recursive thinking process. Specifically, SOCRATIC QUESTIONING leverages large language models to raise and answer sub-questions until collecting enough information to tackle the original question. Unlike CoT,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Advanced Graph Neural Networks
