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。

检索增强生成中的循环 (LoRAG)

Loops On Retrieval Augmented Generation (LoRAG)

Health insurance companies have a defined process called prior authorization
(PA) which is a health plan cost-control process that requires doctors and
other healthcare professionals to get clearance in advance from a health plan
before performing a particular procedure on a patient in order to be eligible
for payment coverage. For health insurance companies, approving PA requests for
patients in the medical domain is a time-consuming and challenging task. One of
those key challenges is validating if a request matches up to certain criteria
such as age, gender, etc. In this work, we evaluate whether GPT can validate
numerous key factors, in turn helping health plans reach a decision drastically
faster. We frame it as a question answering task, prompting GPT to answer a
question from patient electronic health record. We experiment with different
conventional prompting techniques as well as introduce our own novel prompting
technique. Moreover, we report qualitative assessment by humans on the natural
language generation outputs from our approach. Results show that our method
achieves superior performance with the mean weighted F1 score of 0.61 as
compared to its standard counterparts.

医疗保险公司使用先批准（PA）等过程进行费用控制，而本研究评估了 GPT 在验证关键因素方面的有效性，以帮助健康计划更快地做出决策，并发现我们的方法在自然语言生成方面表现出优越的性能。

通过基于准则的自动问答，GPT 能改进事前授权状态吗？

Can GPT Improve the State of Prior Authorization via Guideline Based  Automated Question Answering?

Smartphone applications designed to track human motion in combination with
wearable sensors, e.g., during physical exercising, raised huge attention
recently. Commonly, they provide quantitative services, such as personalized
training instructions or the counting of distances. But qualitative monitoring
and assessment is still missing, e.g., to detect malpositions, to prevent
injuries, or to optimize training success. We address this issue by presenting
a concept for qualitative as well as generic assessment of recurrent human
motion by processing multi-dimensional, continuous time series tracked with
motion sensors. Therefore, our segmentation procedure extracts individual
events of specific length and we propose expressive features to accomplish a
qualitative motion assessment by supervised classification. We verified our
approach within a comprehensive study encompassing 27 athletes undertaking
different body weight exercises. We are able to recognize six different
exercise types with a success rate of 100% and to assess them qualitatively
with an average success rate of 99.3%.

本文介绍了一种结合智能手机应用程序和可穿戴传感器的人体运动定量监测，旨在提高运动员安全性、预防受伤和优化训练效果的质性评估方法，基于时序序列数据进行个体特征提取，并通过监督分类技术的应用成功地评估了 27 名运动员的训练动作类型和质量。