Research on Enhancing Complex Reasoning Capabilities of Large Language Models Based on Chain-of-Thought

Abstract

Addressing the bottlenecks of existing Chain-of-Thought (CoT) methods in complex reasoning of Large Language Models (LLMs), such as reasoning skips, frequent logical illusions, and unbalanced computational costs, this paper proposes an original Adaptive Bidirectional Verification Chain-of-Thought (ABV-CoT) algorithm. This algorithm comprises three core modules: a task complexity-aware forward adaptive reasoning step size generation module, which dynamically matches reasoning granularity with task difficulty; a reverse logical closed-loop verification and error backtracking correction module, which can locate and correct intermediate reasoning errors, reducing logical illusions and error accumulation; and a lightweight gated attention optimization unit, which can improve reasoning performance while controlling computational costs. Comparative and ablation experiments are conducted on four standard complex reasoning datasets, including GSM8K and MATH, using Llama-2-7B, Llama-2-13B, and Qwen-14B as base models. The results show that the ABV-CoT algorithm improves the average inference accuracy by 5.12-19.78 percentage points compared to the mainstream CoT method, reduces inference latency by 36.84% compared to the Tree-of-Thoughts (ToT) method, and reduces memory usage by 26.09%. It exhibits excellent generalization ability on small-to-medium-scale models, providing a new technical path and theoretical support for enhancing the complex reasoning capabilities of LLMs, and has significant academic and engineering value.