"检 索 增 强 生 成 (Retrieval-Augmented Generation,RAG) 是 一 种 有 效 优 化 大 语 言模 型 在 工 艺 规 范 问 答 任 务 中 性 能 的 方 法 。 然 而 , 基 于 固 定 文 本 长 度 分 块 的 朴素RAG(Naive RAG)在构建工艺规范问答任务时表现不佳。主要原因在于工艺规范是一类复杂的技术文档,采用固定文本长度分块会丢失工艺规范段落层级之间的结构关系以及隐含的知识关联关系,导致输出结果质量下降。因此,本文提出了一种利用工艺规范篇章段落间隐含的树结构关系来构建RAG的方法,该方法有效解决了固定文本长度分块导致的段落之间的知识关联丢失问题。实验结果表明,树结构RAG在评价指标上优于朴素RAG,其中ACC平均提升3.81%,ROUGE-L提升3.28%,BLEU-4提升2.97%,验证了树结构RAG的有效性。"

Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community.