LLaMA-LoRA Neural Prompt Engineering: A Deep Tuning Framework for Automatically Generating Chinese Text Logical Reasoning Thinking Chains

Abstract

ABSTRACT The exption of Chinese natural language processing (NLP) has stimulated research in the broader NLP domain. However, existing large language models have limitations in comprehending and reasoning in Chinese. This paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource requirements. We propose LLaMA-LoRA, a neural prompt engineering framework that builds upon the LLaMA-13B model and incorporates the Low-Rank Adaptation (LoRA) of Large Language Models technique for refinement. Chain-of-Thought (CoT) are crucial for generating intermediate reasoning chains in language models, but their effectiveness can be limited by isolated language patterns. Erroneous reasoning resulting from conventional prompts negatively impacts model performance. Automatic prompts are introduced to encourage reasoning chain generation and accurate answer inference. Training the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning abilities. The LLaMA-LoRA model demonstrates exceptional performance across numerous Chinese language tasks, surpassing benchmark performance achieved by related language models such as GPT-3.5, Chat-GLM, and OpenAssistant, delivering accurate, comprehensive, and professional answers. The availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.