【免费】前沿技术论文对话ChatGPT:科技应用和限制

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Let’s have a chat! A Conversation with ChatGPT: Technology,

Applications, and Limitations

Sakib Shahriar

*, Kadhim Hayawi²

School of Computer Science, University of Guelph, Guelph, Ontario, Canada¹

College of Interdisciplinary Studies, Computational Systems, Zayed University, Abu Dhabi, UAE²

Abstract

The emergence of an AI-powered chatbot that can generate human-like sentences and write coherent essays

has caught the world's attention. This paper discusses the historical overview of chatbots and the technology

behind Chat Generative Pre-trained Transformer, better known as ChatGPT. Moreover, potential

applications of ChatGPT in various domains, including healthcare, education, and research, are highlighted.

Despite promising results, there are several privacy and ethical concerns surrounding ChatGPT. In addition,

we highlight some of the important limitations of the current version of ChatGPT. We also ask ChatGPT

to provide its point of view and present its responses to several questions we attempt to answer.

Keywords: Artificial intelligence; Natural Language Processing; ChatGPT; Text Generation; Chatbots

1. Introduction

In 1950, the British computer scientist Alan Turing disputed whether human reasoning can be matched

by computers: “Can machines think?” [1]. Subsequently, he proposed the Turing Test to measure computer

or artificial intelligence. In a Turing test, a human interrogator is presented with responses from a human

and a computer (with the ability to generate written texts in real-time). If the interrogator cannot distinguish

between the answers, the computer system passes the Turing Test. Although several computer programs

and chatbots like Eliza demonstrated success in the Turing test ([2] [3]), these programs arguably used

certain tricks to pass the test [4] rather than demonstrating any significant intelligence. With the

advancement in machine learning and natural language processing (NLP), chatbots have gained significant

research attention and have been used for a variety of commercial and non-commercial applications ([5],

[6], [7], [8], [9]). Despite their vast adoption, most chatbots do not have personalization, and user

This is to indicate the corresponding author.

Email address: shahrias@uoguelph.ca

satisfaction remains questionable [10]. This limitation prompted researchers and developers to focus on

chatbot engagement in making chatbots more conversational.

In November 2022, OpenAI released a novel AI-powered chatbot called ChatGPT

, which gained

significant public attention. Unlike any previous chatbots, ChatGPT has surprised the public with its

coherent responses to various questions. In this short paper, we explore the technology enabling the success

of ChatGPT and highlight several domains and applications of ChatGPT. We also invited ChatGPT to

respond to some of the questions we attempted to answer, starting with its response to the history of

chatbots.

Question: Hi ChatGPT, tell us about the history of chatbots

ChatGPT: Certainly! The idea of a machine that could simulate human conversation dates back several

decades. In fact, the first chatbot was created in the 1960s by MIT computer scientist Joseph Weizenbaum.

Weizenbaum's chatbot, called ELIZA, used a set of rules to simulate conversation by asking questions and

giving scripted responses. ELIZA was designed to imitate a psychotherapist, and people were often

surprised at how convincingly it could simulate human conversation.

Over the years, chatbots have continued to evolve and improve. One of the key advancements was the

development of natural language processing (NLP) technology, which allows chatbots to understand and

respond to human language more accurately. In the 1990s and early 2000s, chatbots began to be used for

customer service and support, with companies like AOL and Microsoft developing chatbots to help users

with their software and services.

In recent years, there has been a surge of interest in chatbots, driven in part by advancements in artificial

intelligence and machine learning. Modern chatbots use a combination of NLP, machine learning, and

other AI technologies to understand and respond to human language in a more natural and nuanced way.

They are now used in a wide range of applications, from customer service and support to personal

assistants and entertainment.

Overall, the history of chatbots is a fascinating one that reflects the evolution of AI and our growing

desire to create machines that can understand and interact with us in more human-like ways.

Our first observation is that the response from ChatGPT appears to be very coherent, engaging, and free

of grammatical errors. We also notice that ChatGPT does well in providing factual information. It manages

to cite names and dates accurately. Interestingly, ChatGPT does not talk about itself, and we understand

that it is limited in presenting information after September 2021

. Finally, it is also captivating to observe

that ChatGPT has ended its response with a summary offering its viewpoint.

https://openai.com/blog/chatgpt/

https://www.forbes.com/sites/ariannajohnson/2022/12/07/heres-what-to-know-about-openais-chatgpt-what-its-

disrupting-and-how-to-use-it/?sh=41280f462643

2. ChatGPT Technology: From Language Models to Reinforcement Learning

Machine learning, a subfield of artificial intelligence (AI), is defined as the ability of computer systems

to learn from experience without being explicitly taught or programmed. With advances in computing

power, increasing data availability, and algorithmic improvements, deep learning has emerged as a high-

performing predictive tool. Unlike traditional machine learning, deep learning algorithms use artificial

neural networks, often in a large capacity. Deep learning models generally require more training data but

can provide better generalization. Transformers or large language models are a special family of deep

learning models that have emerged as state-of-the-art solutions for text data. From text classification to text

generation, these models offer superior performance than other AI models. Language models are usually

pre-trained on large language corpus, such as Wikipedia, but can be fine-tuned for a specific task. The two

most prominent language models include Bidirectional Encoder Representations from Transformers

(BERT) [11] and Generative Pretrained Transformer (GPT) [12]. The former is primarily used for

recognizing the context in language and classification tasks, while the latter is more adept at language

generation tasks such as machine translation and question-answering.

Several chatbots prior to ChatGPT used language models. However, the main component that allowed

ChatGPT its coherent and engaging responses is attributed to the use of reinforcement learning. In this

context, human evaluators ranked the responses of ChatGPT, which allowed it to optimize some of its

parameter to essentially become a better conversationalist. ChatGPT was developed on top of GPT-3, a

generative autoregressive language model with 175 billion parameters [13]. To train such a complex

network, OpenAI partnered with Microsoft with a reported training resource of over 350GB of memory

costing $12 million

Question: Hi ChatGPT, how were you were developed?

ChatGPT: Hello! I was developed by OpenAI, a research organization focused on advancing artificial

intelligence in a safe and beneficial way. Specifically, I was developed by a team of researchers and

engineers at OpenAI who used a deep learning technique called a transformer to train me on a large corpus

of text data. The transformer is a type of neural network that is especially good at processing sequential

data, like language.

To train me, the team fed me huge amounts of text from a variety of sources, including books, articles,

and websites. They then used a process called unsupervised learning to teach me to recognize patterns in

the text data. Through this process, I learned to understand and generate natural language.

After my initial development, I went through a rigorous testing and refinement process to ensure that I

was accurate, reliable, and safe to use. And since my release, I have been continually updated and improved

to better serve my users.

https://venturebeat.com/ai/ai-machine-learning-openai-gpt-3-size-isnt-everything/

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