This paper presents BARKPLUG V.2, a Large Language Model (LLM)-based chatbot
system built using Retrieval Augmented Generation (RAG) pipelines to enhance
the user experience and access to information within academic settings.The
objective of BARKPLUG V.2 is to provide information to users about various
campus resources, including academic departments, programs, campus facilities,
and student resources at a university setting in an interactive fashion. Our
system leverages university data as an external data corpus and ingests it into
our RAG pipelines for domain-specific question-answering tasks. We evaluate the
effectiveness of our system in generating accurate and pertinent responses for
Mississippi State University, as a case study, using quantitative measures,
employing frameworks such as Retrieval Augmented Generation Assessment(RAGAS).
Furthermore, we evaluate the usability of this system via subjective
satisfaction surveys using the System Usability Scale (SUS). Our system
demonstrates impressive quantitative performance, with a mean RAGAS score of
0.96, and experience, as validated by usability assessments.

本文介绍了 BARKPLUG V.2，一个基于大型语言模型（LLM）的聊天机器人系统，利用检索增强生成（RAG）流程提高学术环境下的用户体验和信息获取。该系统利用大学数据作为外部数据语料库，在领域特定的问答任务中吸收这些数据到我们的 RAG 流程中。我们使用定量测量方法，如检索增强生成评估（RAGAS）框架来评估我们的系统在密西西比州立大学作为案例研究时生成准确和相关回答的效果，并通过主观满意度调查，使用系统可用性量表（SUS）评估该系统的可用性。我们的系统在定量性能方面表现出色，平均 RAGAS 得分为 0.96，并通过可用性评估得到验证。

从问题到有见地的回答：建立一个了解大学资源的聊天机器人

From Questions to Insightful Answers: Building an Informed Chatbot for  University Resources

Existing deep learning methods for image deblurring typically train models
using pairs of sharp images and their blurred counterparts. However,
synthetically blurring images do not necessarily model the genuine blurring
process in real-world scenarios with sufficient accuracy. To address this
problem, we propose a new method which combines two GAN models, i.e., a
learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN), in order to
learn a better model for image deblurring by primarily learning how to blur
images. The first model, BGAN, learns how to blur sharp images with unpaired
sharp and blurry image sets, and then guides the second model, DBGAN, to learn
how to correctly deblur such images. In order to reduce the discrepancy between
real blur and synthesized blur, a relativistic blur loss is leveraged. As an
additional contribution, this paper also introduces a Real-World Blurred Image
(RWBI) dataset including diverse blurry images. Our experiments show that the
proposed method achieves consistently superior quantitative performance as well
as higher perceptual quality on both the newly proposed dataset and the public
GOPRO dataset.

本论文提出了一种使用两个 GAN 模型的新方法，即学会如何模糊图像的深度学习模型和学习如何去模糊的深度学习模型，通过学习如何模糊图像来学习更好的图像去模糊模型，并使用相对论模糊损失来降低真实模糊与合成模糊之间的差异。实验表明，该方法在新提出的数据集和公共数据集上都取得了一致优越的定量性能和更高的感知质量。