Core Concepts
The author introduces the Stepwise Self-Consistent Chain-of-Thought (SSC-CoT) algorithm to improve mathematical reasoning using Large Language Models by identifying critical intermediate steps through diverse reasoning chains and a knowledge graph.
Abstract
The content discusses the challenges in using Large Language Models for complex mathematical reasoning, introducing SSC-CoT as a solution. SSC-CoT selects critical intermediate steps based on intersecting reasoning chains and utilizes a knowledge graph. The TriMaster100 dataset is introduced for evaluating complex trigonometry problems. Results show SSC-CoT outperforms other methods on both TriMaster100 and MATH Level 5 datasets.
Key points include the introduction of SSC-CoT to enhance mathematical reasoning, the creation of the TriMaster100 dataset for evaluation, and comparisons with other state-of-the-art methods showing SSC-CoT's superior performance.
SSC-CoT improves LLMs' capabilities in solving complex math problems by identifying critical intermediate steps through diverse reasoning chains and a knowledge graph. The TriMaster100 dataset facilitates evaluation of these methods, demonstrating SSC-CoT's effectiveness.
SSC-CoT surpasses other algorithms in solving complex mathematical questions, showcasing its potential in enhancing mathematical reasoning processes.
Stats
On TriMaster100, SSC-CoT triples the effectiveness of state-of-the-art methods.
On MATH Level 5, SSC-CoT surpasses the second-best method by 7.2% in accuracy.