docs(paper): 简化摘要和关键词部分

移除关键词列表中冗余的术语，使内容更加简洁聚焦。主要保留了核心概念如大语言模型、代码生成等，去掉了QLoRA、Gradio等具体技术细节，以突出研究重点。

paper/latex/chapters/abstract.tex

@@ -9,7 +9,7 @@
 \begin{onecolabstract}
 	\noindent{}{\zihao{4}\textbf{摘要}}{\quad \songti \zihao{-4}大语言模型（LLMs）在通用代码生成任务中表现出色，但在处理包含专有知识的企业私有代码库时，其性能往往受限。针对此问题，本文提出并实现了一个基于文档驱动的自适应编码大模型微调框架。该框架的核心创新在于：首先，通过深度解析技术文档（Markdown格式），自动抽取信息并结合预设模板生成高质量的指令微调（SFT）训练语料；其次，利用参数高效微调技术（如Quantized Low-Rank Adaptation，QLoRA）对预训练的编码大模型（以qwen2.5为例）进行针对性优化，使其精准适配私有库特有的语法结构与编程范式；最后，整合了包括数据持久化（SQLite+TinyDB）、训练监控（TensorBoard）和交互式前端（Gradio）在内的完整工作流。实验结果表明，该框架能够有效提升大模型在私有库代码生成任务上的准确性和实用性，为实现企业级软件开发的智能化和高效化提供了一套自动化、可扩展的解决方案。
 	}\par
-	\noindent{}{\zihao{4}\textbf{关键词}}{\quad \zihao{-4}\songti 大语言模型; 代码生成; 模型微调; 参数高效微调; QLoRA; 文档驱动; 私有库; 自然语言处理; Gradio
+	\noindent{}{\zihao{4}\textbf{关键词}}{\quad \zihao{-4}\songti 大语言模型；代码生成；模型微调；文档驱动
 	}\par
 \end{onecolabstract}
 
@@ -17,6 +17,6 @@
 \begin{onecolabstract}
 	\noindent{}{ \zihao{4} \textbf{ABSTRACT}}{\quad \zihao{-4}Large Language Models (LLMs) excel in general code generation tasks, but their performance is often limited when handling enterprise private code repositories containing proprietary knowledge. To address this issue, this paper proposes and implements a document-driven adaptive fine-tuning framework for large code models. The core innovations of this framework are: first, by deeply parsing technical documentation (Markdown format), it automatically extracts information and combines it with preset templates to generate high-quality instruction fine-tuning (SFT) training data; second, it utilizes parameter-efficient fine-tuning techniques (such as Quantized Low-Rank Adaptation (QLoRA)) to specifically optimize a pre-trained large code model (taking qwen2.5 as an example), enabling it to accurately adapt to the specific syntax, structure, and programming paradigms of the private library; finally, it integrates a complete workflow including data persistence (SQLite+TinyDB), training monitoring (TensorBoard), and an interactive frontend (Gradio). Experimental results demonstrate that this framework can effectively improve the accuracy and practicality of large models in private library code generation tasks, and provide an automated, scalable solution for intelligent and efficient enterprise software development.
 	}\par
-	\noindent{}{ \zihao{4}\textbf{KEYWORDS}}{\quad \zihao{-4}Large Language Models; Code Generation; Model Fine-tuning; Parameter-Efficient Fine-tuning; QLoRA; Document-Driven; Private Library; Natural Language Processing; Gradio
+	\noindent{}{ \zihao{4}\textbf{KEYWORDS}}{\quad \zihao{-4}Large Language Models; Code Generation; Model Fine-tuning; Document-Driven
 	}\par
 \end{onecolabstract}