Retrieval-augmented text generation (RAG) addresses the common limitations of
large language models (LLMs), such as hallucination, by retrieving information
from an updatable external knowledge base. However, existing approaches often
require dedicated backend servers for data storage and retrieval, thereby
limiting their applicability in use cases that require strict data privacy,
such as personal finance, education, and medicine. To address the pressing need
for client-side dense retrieval, we introduce MeMemo, the first open-source
JavaScript toolkit that adapts the state-of-the-art approximate nearest
neighbor search technique HNSW to browser environments. Developed with modern
and native Web technologies, such as IndexedDB and Web Workers, our toolkit
leverages client-side hardware capabilities to enable researchers and
developers to efficiently search through millions of high-dimensional vectors
in the browser. MeMemo enables exciting new design and research opportunities,
such as private and personalized content creation and interactive prototyping,
as demonstrated in our example application RAG Playground. Reflecting on our
work, we discuss the opportunities and challenges for on-device dense
retrieval. MeMemo is available at this https URL

通过在浏览器环境中适应最先进的近似最近邻搜索技术，我们介绍了 MeMemo 这个 JavaScript 工具包，使得研究人员和开发者能够在浏览器中高效地搜索数百万个高维向量。这种工具包提供了新的设计和研究机会，如个人和个性化内容创建以及交互式原型设计，并且具有数据隐私保护的能力。

MeMemo: 设备上的检索增强 用于私密和个性化文本生成

MeMemo: On-device Retrieval Augmentation for Private and Personalized  Text Generation

This paper presents Loops On Retrieval Augmented Generation (LoRAG), a new
framework designed to enhance the quality of retrieval-augmented text
generation through the incorporation of an iterative loop mechanism. The
architecture integrates a generative model, a retrieval mechanism, and a
dynamic loop module, allowing for iterative refinement of the generated text
through interactions with relevant information retrieved from the input
context. Experimental evaluations on benchmark datasets demonstrate that LoRAG
surpasses existing state-of-the-art models in terms of BLEU score, ROUGE score,
and perplexity, showcasing its effectiveness in achieving both coherence and
relevance in generated text. The qualitative assessment further illustrates
LoRAG's capability to produce contextually rich and coherent outputs. This
research contributes valuable insights into the potential of iterative loops in
mitigating challenges in text generation, positioning LoRAG as a promising
advancement in the field.

该研究提出了一种名为 “Loops On Retrieval Augmented Generation（LoRAG）” 的新框架，通过引入迭代循环机制来提高检索增强文本生成的质量。实验结果表明 LoRAG 在 BLEU 分数、ROUGE 分数和困惑度等指标上超过了现有的最先进模型，展示了其在生成文本的连贯性和相关性方面的有效性。定性评估进一步说明了 LoRAG 生成的上下文丰富且连贯的输出能力，为探索迭代循环在文本生成中的应用价值提供了有价值的见解，将 LoRAG 定位为该领域的一个有前途的进展。