Autonomous Question and Answer System Based on ChatGPT With Large Language Model

Qeios Pub Date : 2024-03-17 DOI:10.32388/3n00oc

Jun Wang

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Abstract

Chat-GPT has become very popular in recent years. But there is a problem. Chat-GPT does not ask questions to the users. Therefore, Chat-GPT looks like a machine, not a human. However, users sometimes do not want a single answer. They want real things like food, cars, products, etc. Therefore, our system will ask users questions several times until they get what they really want. In this project, we not only resort to Chat-GPT3.5 to find questions, but also resort to traditional programming skills or databases to solve these problems. OpenAI's Chat-GPT3.5 will play the main role in this project. Furthermore, Java and Spring-Boot will be used in this project. These are mature frameworks for enterprise systems(de Oliveira, C. E., Turnquist, G. L., & Antonov, A., 2018). Finally, the MySQL database is used in this project. It provides comprehensive and reliable SQL database services. The data stored in MySQL instances can generate very large data sets(bin Uzayr, S., 2022). Python3 and some machine learning frameworks such as NumPy, Pandas, TensorFlow and PyTorch are used in this project to analyze user behavior(Liu, Y. H., 2020). In the future, the dataset can be integrated into webtext2 as a basic data element to connect the model training.

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基于大语言模型 ChatGPT 的自主问答系统

Chat-GPT 近年来非常流行。但有一个问题。Chat-GPT 不会向用户提问。因此，Chat-GPT 看起来像机器，而不是人。然而，用户有时并不想要单一的答案。他们想要的是真实的东西，如食品、汽车、产品等。因此，我们的系统会多次向用户提问，直到他们得到真正想要的东西。在这个项目中，我们不仅要借助 Chat-GPT3.5 来寻找问题，还要借助传统的编程技巧或数据库来解决这些问题。OpenAI 的 Chat-GPT3.5 将在本项目中发挥主要作用。此外，本项目还将使用 Java 和 Spring-Boot。这些都是企业系统的成熟框架（de Oliveira, C. E., Turnquist, G. L., & Antonov, A., 2018）。最后，本项目使用 MySQL 数据库。它提供全面可靠的 SQL 数据库服务。存储在 MySQL 实例中的数据可以生成非常大的数据集（bin Uzayr, S., 2022）。本项目使用 Python3 和一些机器学习框架（如 NumPy、Pandas、TensorFlow 和 PyTorch）来分析用户行为（Liu, Y. H., 2020）。未来，该数据集可集成到 webtext2 中，作为连接模型训练的基本数据元素。

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