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

作     者：Songlin Chen Weicheng Wang Xiaoliang Chen Peng lu Zaiyan Yang Yajun Du 

作者机构：School of Computer and Software EngineeringXihua Universitychengdu 610039P.R.china Department of Computer Science and Operations ResearchUniversity of MontrealMontrealQC H3C3J7Canada College of Artificial intelligenceBeijing University of Posts and TelecommunicationsBeijing100876China 

出 版 物：《Data Intelligence》 (数据智能（英文）)

年 卷 期：2024年第6卷第2期

页      面：375-408页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the the Science and Technology Program of Sichuan Province(Grant no.2023YFS0424) the"Open bidding for selecting the best candidates"Science and Technology Project of Chengdu(Grant no.2023-JB00-00020-GX) the National Natural Science Foundation(Grant nos.61902324,11426179,and 61872298) 

主　　题：Chinese natural language processing Neural prompt engineering Large language models Low-Rank adaptation Chain-of-thought 

摘      要：The exption of Chinese natural language processing(NLP)has stimulated research in the broader NLP ***,existing large language models have limitations in comprehending and reasoning in *** paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource *** 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 ***-of-Thought(CoT)are crucial for generating intermediate reasoning chains in language models,but their effectiveness can be limited by isolated language *** reasoning resulting from conventional prompts negatively impacts model *** prompts are introduced to encourage reasoning chain generation and accurate answer *** the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning *** 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 *** availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.