In the rapidly changing world of smart technology, searching for documents
has become more challenging due to the rise of advanced language models. These
models sometimes face difficulties, like providing inaccurate information,
commonly known as "hallucination." This research focuses on addressing this
issue through Retrieval-Augmented Generation (RAG), a technique that guides
models to give accurate responses based on real facts. To overcome scalability
issues, the study explores connecting user queries with sophisticated language
models such as BERT and Orca2, using an innovative query optimization process.
The study unfolds in three scenarios: first, without RAG, second, without
additional assistance, and finally, with extra help. Choosing the compact yet
efficient Orca2 7B model demonstrates a smart use of computing resources. The
empirical results indicate a significant improvement in the initial language
model's performance under RAG, particularly when assisted with prompts
augmenters. Consistency in document retrieval across different encodings
highlights the effectiveness of using language model-generated queries. The
introduction of UMAP for BERT further simplifies document retrieval while
maintaining strong results.

通过 Retrieval-Augmented Generation (RAG) 技术结合语言模型，本研究以智能技术为背景，探讨解决语言模型中的 “hallucination” 问题，并通过使用 Orca2 模型进行查询优化来提高性能。实证结果显示，在 RAG 的帮助下，初始语言模型的性能得到显著改善，并展示了使用语言模型生成查询的效果。引入 BERT 的 UMAP 技术进一步简化了文档检索过程。

增强带增补查询的语言生成的检索过程

Enhancing Retrieval Processes for Language Generation with Augmented  Queries

In recent years, with the increasing popularity of "Smart Technology", the
number of Internet of Things (IoT) devices and systems have surged
significantly. Various IoT services and functionalities are based on the
analytics of IoT streaming data. However, IoT data analytics faces concept
drift challenges due to the dynamic nature of IoT systems and the ever-changing
patterns of IoT data streams. In this article, we propose an adaptive IoT
streaming data analytics framework for anomaly detection use cases based on
optimized LightGBM and concept drift adaptation. A novel drift adaptation
method named Optimized Adaptive and Sliding Windowing (OASW) is proposed to
adapt to the pattern changes of online IoT data streams. Experiments on two
public datasets show the high accuracy and efficiency of our proposed adaptive
LightGBM model compared against other state-of-the-art approaches. The proposed
adaptive LightGBM model can perform continuous learning and drift adaptation on
IoT data streams without human intervention.

提出了自适应物联网数据分析框架，在优化的 LightGBM 和概念漂移适应的基础上实现异常检测。通过实验验证，该框架具有高准确性和效率，并且不需要人类干预即可进行连续学习和漂移自适应。