# One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey
on ChatGPT in AIGC Era

Chaoning Zhang  Kyung Hee UniversitySouth Korea<EMAIL_ADDRESS>,
Chenshuang Zhang  KAISTSouth Korea<EMAIL_ADDRESS>,  Chenghao Li
KAISTSouth Korea<EMAIL_ADDRESS>,  Yu Qiao  Kyung Hee
UniversitySouth Korea<EMAIL_ADDRESS>,  Sheng Zheng  Beijing Institute of
TechnologyChina<EMAIL_ADDRESS>,  Sumit Kumar Dam  Kyung Hee
UniversitySouth Korea<EMAIL_ADDRESS>,  Mengchun Zhang  KAISTSouth Korea
<EMAIL_ADDRESS>,  Jung Uk Kim  Kyung Hee UniversitySouth Korea
<EMAIL_ADDRESS>,  Seong Tae Kim  Kyung Hee UniversitySouth Korea
<EMAIL_ADDRESS>,  Jinwoo Choi  Kyung Hee UniversitySouth Korea
<EMAIL_ADDRESS>,  Gyeong-Moon Park  Kyung Hee UniversitySouth Korea
<EMAIL_ADDRESS>,  Sung-Ho Bae  Kyung Hee UniversitySouth Korea
<EMAIL_ADDRESS>,  Lik-Hang Lee  Hong Kong Polytechnic UniversityHong Kong SAR
(China<EMAIL_ADDRESS>,  Pan Hui  Hong Kong University of Science
and Technology (Guangzhou)China<EMAIL_ADDRESS>,  In So Kweon  KAISTSouth Korea
<EMAIL_ADDRESS>and  Choong Seon Hong  Kyung Hee UniversitySouth Korea
<EMAIL_ADDRESS>
(2022)

###### Abstract.

OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is
demonstrated to be one small step for generative AI (GAI), but one giant leap
for artificial general intelligence (AGI). Since its official release in
November 2022, ChatGPT has quickly attracted numerous users with extensive
media coverage. Such unprecedented attention has also motivated numerous
researchers to investigate ChatGPT from various aspects. According to Google
scholar, there are more than 500 articles with ChatGPT in their titles or
mentioning it in their abstracts. Considering this, a review is urgently
needed, and our work fills this gap. Overall, this work is the first to survey
ChatGPT with a comprehensive review of its underlying technology,
applications, and challenges. Moreover, we present an outlook on how ChatGPT
might evolve to realize general-purpose AIGC (a.k.a. AI-generated content),
which will be a significant milestone for the development of AGI.

Survey, ChatGPT, GPT-4, Generative AI, AGI, Artificial General Intelligence,
AIGC

††copyright: acmcopyright††journalyear: 2022††doi: XXXXXXX.XXXXXXX††price:
15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs: Computing methodologies Computer
vision tasks††ccs: Computing methodologies Natural language generation††ccs:
Computing methodologies Machine learning approaches

###### Contents

  1. 1 Introduction
  2. 2 Overview of ChatGPT
    1. 2.1 OpenAI
    2. 2.2 Capabilities
  3. 3 Technology behind ChatGPT
    1. 3.1 Two core techniques
    2. 3.2 Technology path
  4. 4 Applications of ChatGPT
    1. 4.1 Scientific writing
    2. 4.2 Education field
    3. 4.3 Medical field
    4. 4.4 Other fields
  5. 5 Challenges
    1. 5.1 Technical limitations
    2. 5.2 Misuse cases
    3. 5.3 Ethical concerns
    4. 5.4 Regulation policy
  6. 6 Outlook: Towards AGI
    1. 6.1 Technology aspect
    2. 6.2 Beyond technology
  7. 7 Conclusion

##  1\. Introduction

Figure 1. Structure overview of this survey.

The past few years have witnessed the advent of numerous generative AI (AIGC,
a.k.a. AI-generated content) tools (Ramesh et al., 2021; Radford et al., 2022;
Ji et al., 2020), suggesting AI has entered a new era of creating instead of
purely understanding content. For a complete survey on generative AI (AIGC),
the readers can refer to (Zhang et al., 2023c). Among those AIGC tools,
ChatGPT, which was released in November 2022, has caught unprecedented
attention. It attracted numerous users, and the number of active monthly users
surpassed 100 million within only two months, breaking the user growth record
of other social products (Neethling, 2023). ChatGPT was developed by OpenAI,
which started as a non-profit research laboratory, with a mission of building
safe and beneficial artificial general intelligence (AGI). After announcing
GPT-3 in 2020, OpenAI has gradually been recognized as a world-leading AI lab.
Very recently, It has released GPT-4, which can be seen as one small step for
generative AI, but one giant step for AGI.

Due to its impressive capabilities on language understanding, numerous news
articles provide extensive coverage and introduction, to name a few, BBC
Science Focus (Hughes, 2023), BBC News (Derico, 2023), CNN Business (Kelly,
2023), Bloomberg News (Solon, 2023). Google’s management has issued a “code
red” over the threat of ChatGPT, suggesting that ChatGPT posed a significant
danger to the company, especially to its search service. This danger seems
more difficult to ignore after Microsoft adopted ChatGPT in their Bing search
service. The stock price change also reflects the belief that ChatGPT might
help Bing compete with Google search. Such unprecedented attention on ChatGPT
has also motivated numerous researchers to investigate this intriguing AIGC
tool from various aspects (Sun, 2022; Rudolph et al., 2023). According to our
literature review on google scholar, no fewer than 500 articles include
ChatGPT in their titles or mention this viral term in their abstract. It is
challenging for readers to grasp the progress of ChatGPT without a complete
survey. Our comprehensive review provides a first look into ChatGPT in a
timely manner.

Since the topic of this survey can be regarded as a commercial tool, we first
present a background on the company, i.e. OpenAI, which developed ChatGPT.
Moreover, this survey also presents a detailed discussion of the capabilities
of ChatGPT. Following the background introduction, this work summarizes the
technology behind ChatGPT. Specifically, we introduce its two core techniques:
Transformer architecture and autoregressive pertaining, based on which we
present the technology path of the large language model GPT from v1 to v4
(Radford et al., 2018, 2019; Brown et al., 2020; OpenAI, 2023a). Accordingly,
we highlight the prominent applications and the related challenges, such as
technical limitations, misuse, ethics and regulation. Finally, we conclude
this survey by providing an outlook on how ChatGPT might evolve in the future
towards general-purpose AIGC for realizing the ultimate goal of AGI. A
structured overview of our work is shown in Figure 1.

##  2\. Overview of ChatGPT

First, we provide a background of ChatGPT and the corresponding organization,
i.e., OpenAI, which aims to build artificial general intelligence (AGI). It is
expected that AGI can solve human-level problems and beyond, on the premise of
building safe, trustworthy systems that are beneficial to our society.

###  2.1. OpenAI

OpenAI is a research laboratory made up of a group of researchers and
engineers committed to the commission of building safe and beneficial AGI
(Fronty, 2022). It was founded on December 11, 2015, by a group of high-
profile tech executives, including Tesla CEO Elon Musk, SpaceX President
Gwynne Shotwell, LinkedIn co-founder Reid Hoffman, and venture capitalists
Peter Thiel and Sam Altman (Kay, 2023). In this subsection, we will talk about
the early days of OpenAI, how it became a for-profit organization, and its
contributions to the field of AI.

In the beginning, OpenAI is a non-profit organization (Chatterjee, 2023), and
its research is centered on deep learning and reinforcement learning, natural
language processing, robotics, and more. The company quickly established a
reputation for its cutting-edge research after publishing several influential
papers (OpenAI, 2023b) and developing some of the most sophisticated AI
models. However, to create AI technologies that could bring in money, OpenAI
was reorganized as a for-profit company in 2019 (Coldewey, 2019). Despite
this, the company keeps developing ethical and secure AI alongside creating
commercial applications for its technology. Additionally, OpenAI has worked
with several top tech firms, including Microsoft, Amazon, and IBM. Microsoft
revealed a new multiyear, multibillion-dollar venture with OpenAI earlier this
year (Capoot, 2023). Though Microsoft did not give a precise sum of
investment, Semafor claimed that Microsoft was in discussions to spend up to
$10 billion (Liz Hoffman, 2023). According to the Wall Street Journal, OpenAI
is worth roughly $29 billion (Berber Jin, 2023).

Figure 2. OpenAI products timeline.

From large language models to open-source software, OpenAI has significantly
advanced the field of AI. To begin with, OpenAI has developed some of the most
potent language models to date, including GPT-3 (Lin, 2023), which has gained
widespread praise for its ability to produce cohesive and realistic text in
numerous contexts. OpenAI also carries out research in reinforcement learning,
a branch of artificial intelligence that aims to train robots to base their
choices on rewards and punishments. Proximal Policy Optimization (PPO) (Hui,
2018), Soft Actor-Critic (SAC) (V.Kumar, 2019), and Trust Area Policy
Optimization (TRPO) (van Heeswijk, 2022) are just a few of the reinforcement
learning algorithms that OpenAI has created so far. These algorithms have been
employed to train agents for various tasks, including playing games and
controlling robots. OpenAI has created many software tools up to this point to
assist with its research endeavors, including the OpenAI Gym (Kathuria, 2021),
a toolset for creating and contrasting reinforcement learning algorithms. In
terms of hardware, OpenAI has invested in several high-performance processing
systems, including the DGX-1 and DGX-2 systems from NVIDIA (run.ai, 2023).
These systems were created with deep learning in mind and are capable of
offering the processing power needed to build sophisticated AI models. Except
for ChatGPT, other popular tools developed by OpenAI include DALL-E (Ramesh et
al., 2021) and Whisper (Radford et al., 2022), Codex (Chen et al., 2021). A
summarization of the OpenAI product pipeline is shown in Figure 2.

###  2.2. Capabilities

ChatGPT uses interactive forms to provide detailed and human-like responses to
questions raised by users (Admin, 2023). ChatGPT is capable of producing high-
quality text outputs based on the prompt input text. GPT-4-based ChatGPT plus
can additionally take images as the input. Except for the basic role of a
chatbot, ChatGPT can successfully handle various text-to-text tasks, such as
text summarization (El-Kassas et al., 2021), text completion, text
classification (Kowsari et al., 2019), sentiment (Zhang et al., 2018) analysis
(Medhat et al., 2014), paraphrasing (Madnani and Dorr, 2010), translation
(Dabre et al., 2020), etc.

ChatGPT has become a powerful competitor in search engines. As mentioned in
our introductory section, Google, which supplies the most excellent search
engine in the world, considers ChatGPT as a challenge to its monopoly (Virdi,
2022). Notably, Microsoft has integrated ChatGPT into its Bing search engine,
allowing users to receive more creative replies (Truly, 2023). We see an
obvious distinction between search engines and ChatGPT. That is, search
engines assist users in finding the information they want, while ChatGPT
develops replies in a two-way conversation, providing users with a better
experience.

Other companies are developing similar chatbot products, such as LamMDA from
Google and BlenderBot from Meta. Unlike ChatGPT, the LaMDA, developed by
Google in 2021, actively participates in conversations with users, resulting
in racist, sexist, and other forms of bias in output text (Nguyen, 2022).
BlenderBot is Meta’s chatbot, and the feedback from users is relatively dull
because the developer has set tighter constraints on its output material
(Piper, 2022). ChatGPT appears to have balanced the human-like output and bias
to some level, allowing for more exciting responses. Significantly, in
addition to being more efficient and having a higher maximum token limit than
vanilla ChatGPT, ChatGPT powered by GPT-4 can create multiple dialect
languages and emotional reactions, as well as reduce undesirable results,
thereby decreasing bias (Terrasi, 2023). It is noted in (Lipenkova, 2023) that
the modeling capacity of ChatGPT can be further improved by using multi-task
learning and enhancing the quality of training data.

##  3\. Technology behind ChatGPT

###  3.1. Two core techniques

Backbone architecture: Transformer. Before the advent of Transformer (Vaswani
et al., 2017), RNN was a dominant backbone architecture for language
understanding, and attention was found to be a critical component of the model
performance. In contrast to prior works that only use attention as a
supportive component, the Google team made a claim in their work title:
“Attention is All You Need” (Vaswani et al., 2017) claimed that since Google
released a paper, namely “Attention is All You Need” (Vaswani et al., 2017) in
2017, research and use of the Transformer backbone structure has experienced
explosive growth in the deep learning community. Therefore, we present a
summary of how the Transformer works, with a focus on its core component
called self-attention.

The underlying principle of self-attention posits that given an input text,
the mechanism is capable of allocating distinct weights to individual words,
thereby facilitating the capture of dependencies and contextual relationships
within the sequence. Each element within the sequence possesses its unique
representation. To calculate the relationship of each element to others within
the sequence, one computes the Q (query), K (key), and V (value) matrices of
the input sequence. These matrices are derived from the linear transformations
of the input sequence. Typically, the query matrix corresponds to the current
element, the key matrix represents other elements, and the value matrix
encapsulates information to be aggregated. The association weight between the
current element and other elements is determined by calculating the similarity
between the query and key matrices. This is generally achieved through a dot
product operation. Subsequently, the similarity is normalized to ensure that
the sum of all associations equals 1, which is commonly executed via the
softmax function. The normalized weights are then applied to the corresponding
values, followed by the aggregation of these weighted values. This process
results in a novel representation that encompasses the association information
between the current word and other words in the text. The aforementioned
process can be formally expressed as follows:

(1) $Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V.$

Transformer techniques have become an essential foundation for the recent
development of large language models, such as BERT (Devlin et al., 2019) and
GPT (Radford et al., 2018, 2019; Brown et al., 2020; OpenAI, 2023a) series are
also models based on Transformer techniques. There is also a line of works
extending Transformer from language to visuals, i.e., computer vision
(Dosovitskiy et al., 2020; He et al., 2022; Liu et al., 2021), which suggests
that Transformer has become a unified backbone architecture for both NLP and
computer vision.

Generative pretraining: Autoregressive. For model pertaining (He et al., 2020;
Zhang et al., 2022a, d, c, b), there are multiple popular generative modeling
methods, including energy-based models (Grenander and Miller, 1994; Vincent,
2011; Song and Ermon, 2019; Song et al., 2020), variational autoencoder
(Kingma and Welling, 2013; Oussidi and Elhassouny, 2018; Altosaar, 2016), GAN
(Goodfellow et al., 2014; Brock et al., 2018; Xia et al., 2022), diffusion
model (Croitoru et al., 2022; Cao et al., 2022; Zhang et al., 2023b, d, a),
etc. Here, we mainly summarize autoregressive modeling methods (Bengio et al.,
2000; Larochelle and Murray, 2011, 2011; Uria et al., 2013, 2016) as they are
the foundation of GPT models (Radford et al., 2018, 2019; Brown et al., 2020;
OpenAI, 2023a).

Autoregressive models constitute a prominent approach for handling time series
data in statistical analysis. These models specify that the output variable is
linearly dependent on its preceding values. In the context of language
modeling (Radford et al., 2018, 2019; Brown et al., 2020; OpenAI, 2023a),
autoregressive models predict the subsequent word given the previous word, or
the last probable word given the following words. The models learn a joint
distribution of sequence data, employing previous time steps as inputs to
forecast each variable in the sequence. The autoregressive model posits that
the joint distribution $p_{\theta}(x)$ can be factorized into a product of
conditional distributions, as demonstrated below:

(2)
$p_{\theta}(x)=p_{\theta}(x_{1})p_{\theta}(x_{2}|x_{1})...p_{\theta}(x_{n}|x_{1},x_{2},...,x_{n-1}).$

While both rely on previous time steps, autoregressive models diverge from
recurrent neural network (RNN) architectures in the sense that the former
utilizes previous time steps as input instead of the hidden state found in
RNNs. In essence, autoregressive models can be conceptualized as a feed-
forward network that incorporates all preceding time-step variables as inputs.

Early works modeled discrete data employing distinct functions to estimate the
conditional distribution, such as logistic regression in Fully Visible Sigmoid
Belief Network (FVSBN)(Gan et al., 2015) and one hidden layer neural networks
in Neural Autoregressive Distribution Estimation (NADE)(Larochelle and Murray,
2011). Subsequent research expanded to model continuous variables (Uria et
al., 2013, 2016). Autoregressive methods have been extensively applied to
other fields with representative works: PixelCNN (Van den Oord et al., 2016)
and PixelCNN++(Salimans et al., 2017)), audio generation (WaveNet(van den Oord
et al., 2016)).

###  3.2. Technology path

The development of ChatGPT is based on a series of GPT models, which
constitute a substantial achievement for the field of NLP. An overview of this
development is summarized in Figure 6. In the following, we summarize the key
components of GPT as well as the major changes in the updated GPTs.

Table 1. Comparison between GPT and BERT. Category | Description
---|---
Similarities |
Backbone |  Both GPT and BERT use attention-based Transformer.
Learning Paradigm |  Both GPT and BERT use self-supervised learning.
Transfer-Learning |  Both GPT and BERT can be fine-tuned for downstream tasks.
Differences |
Text context |  GPT uses unidirectional text context, while BERT uses bidirectional text context.
Architecture |  GPT uses a decoder architecture, while BERT uses an encoder architecture.
Pre-training Strategy |  GPT uses autoregressive modeling, while BERT uses masked language modeling.

BERT v.s. GPT. Traditional language models (Kim et al., 2016; Verwimp et al.,
2017; Miyamoto and Cho, 2016) mainly focused on a particular task and could
not be transferred to other tasks. Transfer learning is a common approach for
alleviating this issue by pretraining a foundation model (Zhou et al., 2023),
which can then be finetuned on various downstream tasks. Based on the
architecture, there are three classes: encoder-decoder (Lewis et al., 2019;
Song et al., 2019; Qi et al., 2020; Raffel et al., 2020), encoder-only (Devlin
et al., 2018; Lan et al., 2019; Liu et al., 2019; Clark et al., 2020),
decoder-only (Radford et al., 2018, 2019; Brown et al., 2020; OpenAI, 2023a).
Out of numerous large language models, encoder-only BERT (Devlin et al., 2018)
and decoder-only GPT (Radford et al., 2018) are arguably the two most popular
ones. A comparison of them is summarized in Table 1. Both of them use
attention-based Transformer (Vaswani et al., 2017) with self-supervised
learning to learn from textual datasets without labels. After pretraining,
both BERT and GPT can be finetuned and show competitive performance in
downstream tasks. A core difference between BERT and GPT lies in their
pretraining strategy: masked modeling (see (Zhang et al., 2022b) for a
complete survey on masked autoencoder) and autoregressive modeling. With
masked modeling, BERT predicts masked language tokens from unmasked ones. A
major advantage of BERT is that it can utilize bidirectional text information,
which makes it compatible with sentiment analysis tasks. Due to the
discrepancy between the mask-then-predict pertaining task and downstream
tasks, BERT is rarely used for the downstream task without finetuning. By
contrast, autoregressive modeling methods (represented by GPT) show
competitive performance for few-shot or zero-shot text generation. In the
following, we summarize the development path of GPT from v1 to v4, which is
shown in 6.

Figure 3. Timeline of GPT model families.

GPT-1. With only the decoder, GPT-1 adopts a 12-layer Transformer and has 117M
parameters (Radford et al., 2018). An overview of GPT-1 and how it can be used
for various downstream tasks is shown in Figure 4. Trained on a massive
BooksCorpus dataset encompassing unique unpublished books, GPT-1 is capable of
grasping long-range dependencies contexts. The general task-agnostic GPT model
outperforms models trained for specific tasks in 9 of 12 tasks, including
natural language inference, question answering, semantic similarity, and text
classification (Radford et al., 2018). The observation that GPT-1 performs
well on various zero-shot tasks demonstrates a high level of generalization.
GPT-1 has evolved into a powerful model for various NLP tasks before the
release of GPT-2.

Figure 4. (left) Transformer architecture and training objectives used in
GPT-1. (right) Input transformations for fine-tuning on different tasks
(figure obtained from (Radford et al., 2018)).

GPT-2. As the successor to GPT-1, GPT-2 was launched by OpenAI in 2019 and
focused on learning NLP tasks without explicit supervision. Similar to GPT-1,
GPT-2 is based on the decoder-only Transformer model. However, the model
architecture and implementation of GPT-2 have been developed, with 1.5 billion
parameters and a trained dataset of 8 million web pages, which are more than
10 times compared to its predecessor GPT-1 (Radford et al., 2019). With a
zero-shot setting, GPT-2 achieved state-of-the-art results on 7 of 8 language
modeling datasets tested, where the 7 datasets’ tasks include performance
recognition for different categories of words, the ability of the model to
capture long-term dependencies, commonsense reasoning, reading comprehension,
summarization, and translation (Radford et al., 2019). However, GPT-2 still
performs poorly on the task of question answering, demonstrating the
capability of unsupervised model GPT-2 needs to be improved (Radford et al.,
2019).

GPT-3. The foundation of GPT-3 is the Transformer architecture, specifically
the GPT-2 architecture. Compared to GPT-2, which had 1.5 billion parameters,
GPT-3 has 175 billion parameters, 96 attention layers, and a 3.2 M batch size,
a significant increase in size (Brown et al., 2020). GPT-3 was trained on a
diverse range of online content, including novels, papers, and websites, using
language modeling, a type of unsupervised learning where the model attempts to
guess the next word in a phrase given the preceding word. After completion,
GPT-3 can be fine-tuned on specific tasks using supervised learning, where
task-specific smaller datasets are employed to train the model, such as text
completion or language translation. Developers can use the GPT-3 model for
numerous applications, including chatbots, language translation, and content
production, thanks to OpenAI’s API (Dale, 2021). The API provides different
access levels depending on the scale and intricacy of the tasks. Compared to
other language models whose performance highly depends on fine-tuning, GPT-3
can perform many tasks (such as language translation) without any such fine-
tuning, gradient, or parameter updates making this model task-agnostic (Mai et
al., 2022).

GPT-3.5. GPT-3.5 is a variation of the widely popular GPT-3 and the ChatGPT is
a fine-tuned version of GPT-3.5. On top of GPT-3 model, GPT-3.5 has extra
fine-tuning procedures: supervised finetuning and termed reinforcement
learning with human feedback (RLHF) (Ye et al., 2023), which are shown in
Figure 5, where the machine learning algorithm receives user feedback and uses
them to align the model. RLHF is used to overcome the limitations of
traditional unsupervised and supervised learning, which can only learn from
unlabeled or labeled data. Human feedback can take different forms, including
punishing or rewarding the model’s behaviors, assigning labels to unlabeled
data, or changing model parameters. By incorporating human feedback into the
training process, GPT-3.5 has a significantly higher usability.

Figure 5. How GPT-3.5 is trained. Image obtained from (Ouyang et al., 2022)).

GPT-4. On March 14, 2023, OpenAI released GPT-4 (OpenAI, 2023a), the fourth
installment in the GPT series. GPT-4 is a large multimodal model capable of
taking text and images as inputs and generating text as output. The model
delivers performance at a human level on several professional and career
standards, but in real-world situations, it is still way less competent than
humans. For example, the virtual bar exam result for GPT-4 is in the top 10%
of test participants, as opposed to the score for GPT-3.5, which was in the
lowest 10% (Katz et al., 2023). The capacity of GPT-4 to follow human
intention is significantly better than that of earlier versions (Ouyang et
al., 2022). The answers by GPT-4 were favored over the responses produced by
GPT-3.5 on 70.2% of the 5,214 questions in the sample provided to ChatGPT and
the OpenAI API. After the overwhelming majority of its pre-training data ends
in September 2021, GPT-4 usually lacks awareness of what has happened and does
not learn from its experiences. It occasionally exhibits basic logical
mistakes that do not seem consistent with its skill in various areas, or it
may be excessively trusting when taking false claims from a user (OpenAI,
2023a). It may struggle with complex issues in the same way that people do,
such as producing code that contains security flaws (OpenAI, 2023a). A
summarization of the model parameters and training dataset for GPT models from
v1 to v4 is shown in Table 2.

Table 2. Parameters and Datasets of GPT Models. N.A. indicates that there is
no public disclosure.

GPT Models |  GPT-1 |  GPT-2 |  GPT-3 |  GPT-3.5 |  GPT-4
---|---|---|---|---|---
Parameters ($10^{9}$) |  0.117 |  1.5 |  175 |  N.A. |  N.A
Dataset |  BooksCorpus (over 40GB) |  WebText (40TB) |  Common Crawl (45TB) |  N.A |  N.A

##  4\. Applications of ChatGPT

###  4.1. Scientific writing

ChatGPT is widely recognized for its powerful content generation capabilities,
which have a significant impact on writing in the academic field. Many
existing works have tested how ChatGPT can be applied to scientific writing,
including brainstorming, literature review, data analysis, direct content
generation, grammar checking, and serving as an academic reviewer.

Brainstorming. Brainstorming is an essential approach for obtaining initial
ideas that are a prerequisite for high-quality scientific research. ChatGPT
can play a variety of roles in brainstorming, ranging from stimulating
creativity (Raftis, 2023; Gunawan, 2023) for new idea generation to providing
suggestions (Temsah et al., 2023; Liu et al., 2023b) for expanding existing
ideas. ChatGPT can assist users in divergent and creative thinking (Raftis,
2023). In addition, some studies have explored ChatGPT’s insights on future
nursing research in a Q&A format, which can analyze the impact of future
technological developments on nursing practice, and provide valuable insights
for nurses, patients, and the healthcare system (Gunawan, 2023). Moreover,
ChatGPT also demonstrates the ability to “think” from multiple perspectives,
it can analyze and reflect on the impact of excess deaths after the COVID-19
pandemic from multiple dimensions such as the medical system, social economy,
and personal health behaviors (Temsah et al., 2023). To evaluate whether
ChatGPT generates useful suggestions for researchers in certain domains. The
authors tested its ability on clinical decision support in (Liu et al., 2023b)
and assessed its difference compared to human-generated suggestions. The test
results have shown that, unlike human thinking, the suggestions generated by
ChatGPT provide a unique perspective, and its generations are evaluated as
highly understandable and relevant, which have significant value in scientific
research.

Literature review. A comprehensive literature review requires covering all
relevant research, which can consume too much time and energy for researchers.
For example, the Semantic Scholar search engine, an AI-based scientific
literature research tool, has indexed more than 200 million scholarly
publications. As a result, finding relevant research papers and extracting key
insights from them is almost like finding a needle in a haystack. Fortunately,
ChatGPT, as an AI-driven research reading tool, can help us browse through a
large number of papers and understand their content. In actual use, we can
give a topic to ChatGPT, then it can help us find out the related literature.
Before discussing the ability of ChatGPT in handling the literature review, we
review a similar AI tool, SciSpace Copilot, which can help researchers quickly
browse and understand papers (Saikiran Chandha, 2023). Specifically, it can
provide explanations for scientific texts and mathematics including follow-up
questions with more detailed answers in multiple languages, facilitating
better reading and understanding of the text. By comparison, ChatGPT as a
general language model not only has all the functions of SciSpace Copilot, but
also can be widely used in various natural language processing scenarios
(Saikiran Chandha, 2023). A literature review is essential for summarizing
relevant work in the selected field. As an exploratory task, they chose the
topic of “Digital Twin in Healthcare” and compile abstracts of papers obtained
from Google Scholar search results using the keywords “digital twin in
healthcare” for the last three years (2020, 2021, and 2022). These abstracts
are then paraphrased by ChatGPT, the generated results are promising (Aydın
and Karaarslan, 2022). However, the application of ChatGPT in this task is
still at the beginning. The authors in (Haman and Školník, 2023) ask ChatGPT
to provide 10 groundbreaking academic articles with DOIs in the field of
medical domains. Unfortunately, after conducting five tests, the results show
that out of the 50 DOIs provided, only 8 of them exist and have been correctly
published. Although ChatGPT’s abilities in the literature review are still
weak, we believe that in the near future, ChatGPT will be widely used for
literature review, further improving the efficiency of researchers and
enabling them to focus their time on key research.

Data analysis. Scientific data needs to be cleaned and organized before being
analyzed, often consuming days or even months of the researcher’s time, and
most importantly, in some cases, having to learn to use a coding language such
as Python or R. The use of ChatGPT for data processing can change the research
landscape. For example, as shown in (Macdonald et al., 2023), ChatGPT
completes the task of data analysis for a simulated dataset of 100,000
healthcare workers of varying ages and risk profiles to help determine the
effectiveness of vaccines, which significantly speeds up the research process
(Macdonald et al., 2023). Another similar AI tool for data analysis is
discussed in (Saikiran Chandha, 2023), where AI-based spreadsheet bots can
convert natural language instructions into spreadsheet formulas. Furthermore,
platforms like Olli can also visualize data, where users only need to simply
describe the desired content, and then they can get AI-created line graphs,
bar graphs, and scatter graphs. Considering that ChatGPT is the most powerful
AI tool so far, we believe that these functions can also be implemented in
ChatGPT in a more intelligent way.

Content generation. Numerous works have attempted to use ChatGPT for content
generation for their articles (Rodriguez, 2022; Alkaissi and McFarlane, 2023).
For example, (Alkaissi and McFarlane, 2023) employed ChatGPT to aid in writing
reports in medical science about the pathogenesis of two diseases.
Specifically, ChatGPT provides three aspects about the mechanism of
homocystinuria-associated osteoporosis, all of which are proven true. However,
when it comes to the references to the generated information, the papers
mentioned by ChatGPT do not exist. (Zhong et al., 2023) described a study on
writing a catalysis review article using ChatGPT, with the topic set to CO2
hydrogenation to higher alcohols. The ChatGPT-generated content includes the
required sections of the paper but lacks an introduction to the reaction
mechanism, which is critical for the topic. The content of this article
contains abundant useful information, but specific details are absent and
certain errors exist. In addition, ChatGPT can help prepare manuscripts, but
the generated results have a large difference from actual published content. A
possible reason is that the keywords of ChatGPT and human-generated text vary
greatly, which requires users to further edit the generated content (Kutela et
al., 2023). ChatGPT has also been utilized to generate a review article in
specific areas such as the health field (Aydın and Karaarslan, 2022), which
indicates scholars can focus on core research while leaving the less creative
part to AI tools. Nonetheless, Considering the style difference between human-
generated content and ChatGPT-generated content, it is suggested in (Kutela et
al., 2023; Aydın and Karaarslan, 2022) to not fully rely on ChatGPT. utilize
ChatGPT as an assistant to help us to complete the writing rather than relying
solely on it.

Proofreading. Before the advent of ChatGPT, there are numerous tools for
grammar check. Some works (Kim, 2022; writecream, 2022; Maximov, 2023) have
conducted tests on grammar and spelling correction, which shows that ChatGPT
provides a better user experience than other AI tools. For example, ChatGPT
can be used to automatically fix any punctuation and grammar mistakes to
improve the writing quality (writecream, 2022). In addition, the study
investigates how ChatGPT can go beyond helping users check grammar and can
further generate reports about document statistics, vocabulary statistics,
etc, change the language of a piece to make it suitable for people of any age,
and even adapt it into a story (Kim, 2022). Another minor but noteworthy point
is that as of now, Grammarly’s advanced version, Grammarly Premium, requires
users to pay a monthly fee of $30, which is relatively more expensive compared
to ChatGPT Plus’s monthly fee of $20. Moreover, ChatGPT has been compared to
other AI-based grammar checkers, including QuillBot, DeepL, DeepL Write, and
Google Docs. The results show that ChatGPT performs the best in terms of the
number of errors detected. While ChatGPT has some usability issues when it
comes to proofreading, such as being over 10 times slower than DeepL and
lacking in the ability to highlight suggestions or provide alternative options
for specific words or phrases (Maximov, 2023), it should be noted that
grammar-checking is just the tip of the iceberg. ChatGPT can also be valuable
in improving language, restructuring text, and other aspects of writing.

Academic reviewer. Peer review of research papers is a crucial process for the
dissemination of new ideas, with a significant impact on scientific progress.
However, the sheer volume of research papers being produced has posed a
challenge for human reviewers. The potential of ChatGPT for literature review
has been investigated in (Srivastava, 2023). Specifically, ChatGPT is capable
of analyzing inputted academic papers, and then it can evaluate them based on
several aspects, including the summary, strengths and weaknesses, clarity,
quality, novelty, and reproducibility of the papers. Furthermore, the
generated reviews of the papers are then inputted into ChatGPT for sentiment
analysis. After this, a decision can be made on the acceptance of the reviewed
paper.

###  4.2. Education field

With the impressive capability to generate human-like responses, ChatGPT has
been studied by numerous works to investigate the impact it brings to the
education field. Here, we summarize them from two perspectives:
teaching/learning and subjects.

Teaching and learning. In a typical classroom setting, the teachers are the
source of knowledge, while the students play the role of knowledge receiver.
Outside the classroom, the students are often required to complete the
assignments designed by the teacher. How the teachers and students interact
with each other can be significantly changed by ChatGPT (Baidoo-Anu and Owusu
Ansah, 2023; Rospigliosi, 2023; Zentner, 2022a; Zhang, 2023).

ChatGPT can revolutionize the paradigm of teaching by providing a wealth of
resources to aid in the creation of personalized tutoring (Zentner, 2022b),
designing course material (Pettinato Oltz, 2023), assessment and evaluation
(Zentner, 2022a; Baidoo-Anu and Owusu Ansah, 2023). Multiple works (Baidoo-Anu
and Owusu Ansah, 2023; Zhang, 2023) have discussed how ChatGPT can be used to
create an adaptive learning platform to meet the needs and capabilities of
students. It has been shown in (TOPSAKAL and TOPSAKAL, 2023) that the teacher
can exploit ChatGPT to guide students in interactive dialogues to help them
learn a new language. ChatGPT has also been utilized to design course material
in law curriculum, such as generating a syllabus and hand-outs for a class, as
well as creating practice test questions (Pettinato Oltz, 2023). Moreover, a
recent work (Pettinato Oltz, 2023) provides preliminary evidence that ChatGPT
can be applied to assist law professors to help scholarship duties.
Specifically, this includes submitting a biography for a speaking engagement,
writing opening remarks for a symposium, and developing a document for a law
school committee. In addition, it is shown in (Zentner, 2022a; Baidoo-Anu and
Owusu Ansah, 2023; Zhang, 2023) that ChatGPT can be exploited as an assessment
and evaluation assistant, including automated grading and performance and
engagement analysis for students.

ChatGPT, on the other hand, also brings a significant impact on how students
learn. A poll (Tangermann, 2023) done by Study.com (an online course provider)
reveals how ChatGPT is used among adult students. According to its findings
(Tangermann, 2023), $89\%$ of them utilized ChatGPT for homework, and $48\%$
of them exploited it for an at-home test or quiz. Moreover, over half of them
admitted to using ChatGPT to write essays, and $22\%$ confessed to using
ChatGPT to create a paper outline. Meanwhile, multiple works (Zentner, 2022a;
Baidoo-Anu and Owusu Ansah, 2023; Zhang, 2023) have investigated how ChatGPT
might assist students in their studies. For example, (Zentner, 2022a; Baidoo-
Anu and Owusu Ansah, 2023) utilize ChatGPT to translate language, which helps
students converse more effectively in academic issues and comprehend different
language essays and papers. Moreover, ChatGPT can be used to propose suitable
courses, programs, and publications to students based on their interests. In
(Zhang, 2023), ChatGPT helps students comprehend certain theories and concepts
to assist in more effective problem-solving.

ChatGPT for various subjects in education. In modern education, there is a
wide variety of subjects, including economics, law, physics, data science,
mathematics, sports, psychology, engineering, and media education, etc. Even
though ChatGPT is not specifically designed to be a master of any specific
subject, it has been demonstrated in numerous works that ChatGPT has a decent
understanding of a certain subject, sometimes surpassing the human level. To
facilitate the discussion, we divide the subjects into STEM (Science,
Technology, Engineering, Mathematics) and non-STEM (including economics, law,
psychology, etc).

STEM subjects. Here, we will discuss the application of ChatGPT in physics,
mathematics, and engineering education. ChatGPT is utilized in (Yeadon et al.,
2022) to create short-form Physics essays that get first-class scores when
assessed using an authorized assessment method. Specifically, the score
ChatGPT-generated essays have a score of 71 $\pm$ 2%, compared to the current
module average of 71 $\pm$ 5%, showcasing its remarkable capacity to write
short-form Physics essays. The statistical analysis of four difficult datasets
is presented in the work (Noever and McKee, 2023) to demonstrate ChatGPT’s
data science capacity, where it can comprehend the true number buried behind
sentence completion. For instance, based on the phrase “Boston housing
dataset,” ChatGPT can provide a tabular blend of category and numerical data
for house value prediction. In (Frieder et al., 2023), ChatGPT can be used to
search for mathematical objects and related information, which outperforms
other mathematical models on Reverse Definition Retrieval. Although ChatGPT
can provide meaningful proof in a few circumstances, it regularly performs
poorly in advanced mathematics. Simultaneously, ChatGPT has sparked
substantial interest in engineering education among both students and
educators. As the work (Qadir, 2022) suggests, the ChatGPT gives insights for
many questions, such as discussing how to use ChatGPT in engineering education
from the viewpoints of students and professors.

Non-STEM subjects Beyond medical standardized tests, the investigation of
ChatGPT on its potential in economics and law exams have also been conducted.
(Geerling et al., 2023) evaluate the performance of ChatGPT for the Test of
Understanding in College Economics (TUCE), which is a undergraduate-lvel
economics test in the United States. The results demonstrate that ChatGPT
properly answers 63.3% of the microeconomics questions and 86.7% of the
macroeconomics questions, which performs better than the average level of
performance of students. The research (Choi et al., 2023) conducted by
Jonathan focused on the performance of ChatGPT on four genuine legal
examinations at the University of Minnesota, the content of which includes 95
multiple-choice questions and 12 essay questions. The study reveals that
ChatGPT passed all four courses and performed at the level of a C+ student.
Moreover, this research mentions that the ChatGPT can be utilized to create
essays with the capacity to comprehend essential legal norms and continuously
solid arrangement. There are a few studies on the application of ChatGPT in
psychology. ChatGPT, as a strong text-generating chatbot, makes it easy to
write essays about psychology (Uludag, 2023). Furthermore, this editorial
(Uludag, 2023) discusses the ChatGPT can help people to socialize and give
feedback about certain situations. However, the ability of ChatGPT to handle
emotional input is still unknowable. The capabilities of ChatGPT have also
been demonstrated in (Pavlik, 2023) to generate articles for journalism and
media.

###  4.3. Medical field

Medical knowledge assessment. The capabilities of ChatGPT in the medical field
have been assessed in several works (Hulman et al., 2023; Yeo et al., 2023;
Gilson et al., 2022; Duong and Solomon, 2023). For example, the skills in
answering questions regarding cirrhosis and hepatocellular carcinoma (HCC)
have been evaluated in (Yeo et al., 2023). The results show that ChatGPT can
answer some basic questions about diagnosis and prevention, and the accuracy
rate for quality measurement questions is 76.9%, but there is still a lack of
understanding of advanced questions such as treatment time and HCC screening
criteria. In addition, ChatGPT is evaluated for its performance on the United
States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams in
(Gilson et al., 2022). Multiple choice questions from the USMLE Step 1 and
Step 2 exams are employed, and the results reveal that the response from the
ChatGPT is equal to that of a third-year medical student (Gilson et al.,
2022). Moreover, (Kung et al., 2023) is another study that evaluates the
competence of ChatGPT on the USMLE in a more comprehensive manner,
encompassing all three tests. In this test, the zero-shot ChaGPT performs
well, with scores above the average. Like the USMLE, many nations have their
own standardized tests in medicine, and the performances of ChatGPT on these
exams (Carrasco et al., 2023; Huh, 2023; Wang et al., 2023a) are tested with
the goal of completely analyzing its capabilities. ChatGPT’s performance on
the MIR exam for Specialized Health Training in Spain is being evaluated
(Carrasco et al., 2023). Furthermore, as the essay (Hulman et al., 2023)
investigated, ChatGPT shows its effectiveness in answering frequently asked
questions about diabetes. Specifically, given 10 questions to both human
experts and ChatGPT, participants are asked to distinguish which answers are
given by the machine or the human. Their results show that participants were
able to distinguish between answers generated by ChatGPT and those written by
humans. Notably, those who have previously used ChatGPT have a greater
likelihood of being able to distinguish between the two. This further
indicates that ChatGPT has the potential to solve medical problems, but it
should be noted that the generated content has its own fixed style. These
studies have shown that ChatGPT can be used for answering questions from
students, providing medical assistance, explaining complex medical concepts,
and responding to inquiries about human anatomy. ChatGPT is also accessed in
(Duong and Solomon, 2023) to offer answers to genetics-related questions. The
result demonstrates that there is no significant difference between the
responses of ChatGPT and those of humans. However, ChatGPT lacks critical
thinking and thus cannot generate counter-arguments for incorrect answers,
which is different from humans.

Disease diagnosis and treatment. Although some machine learning algorithms
have been applied to assist disease analysis, most cases are mainly limited to
single-task-related image interpretation. In this part, we discuss the
capability of ChatGPT in clinical decision support. For example, a study is
conducted in (Rao et al., 2023a) to identify appropriate imaging for patients
requiring breast cancer screening and assessment for breast pain. They compare
the responses of ChatGPT to the guidelines provided by the American College of
Radiology (ACR) for breast pain and breast cancer screening by assessing
whether the proposed imaging modality complies with ACR guidelines. The
results are exciting, with the worst-performing set of metrics achieving an
accuracy of 56.25%. In addition, the clinical decision support capability of
ChatGPT in standardized clinical vignettes, which are a special type of
clinical teaching case primarily used to measure trainees’ knowledge and
clinical reasoning abilities, is evaluated in (Rao et al., 2023b). The authors
input all 36 published clinical cases from the Merck Sharpe & Dohme (MSD)
clinical manual into ChatGPT, and compared the accuracy of ChatGPT in
differential diagnosis, final diagnosis, etc., according to different
classifications of patients. The results showed that ChatGPT achieved an
overall accuracy of 71.7% across all the published clinical cases. Another
similar study on ChatGPT in disease-aided diagnosis is conducted by (Duong and
Solomon, 2023). They provide ChatGPT with 45 vignettes and ask ChatGPT to pick
the correct diagnosis from the top three options in 39 of them. The result is
that it can achieve an accuracy of 87%, which beats the previous study’s
(Mehrotra, 2023) accuracy of 51% based on symptom checkers, on the basis of
data collection through websites or smartphone apps where users answer
questions and subsequently get the recommendation or right care quickly. On
the other hand, in order to provide patients with more accurate diagnoses and
better treatment outcomes, it is necessary to manage and analyze patient
medical data effectively, perhaps leading to better healthcare ultimately.
Therefore, to achieve this, one possible approach is to utilize ChatGPT to
summarize the huge and complex patient medical records and then extract
important information, allowing doctors to quickly understand their patients
and reduce the risk of human error in decision-making (Sallam, 2023). Another
way is to use ChatGPT to translate doctors’ clinical notes into patient-
friendly versions, reducing communication costs for patients and doctors (Khan
et al., 2023). However, it should be emphasized that, as mentioned above,
although ChatGPT has shown its strong capabilities in disease-aided diagnosis
or question answering, unknowns and pitfalls still exist. We recommend readers
seek medical attention from a licensed healthcare professional, when they are
experiencing symptoms or concerns about their health. As a question to ChatGPT
“Can you help me diagnose a disease?”, it answers that: “Only a licensed
healthcare professional can diagnose a disease after a proper medical
evaluation, including a physical examination, medical history, and diagnostic
tests.”

###  4.4. Other fields

Assisted software development. As shown in (Surameery and Shakor, 2023;
Castelvecchi, 2022; Amos, 2023), ChatGPT also has the potential to
revolutionize the way how code developers work in the software industry.
Specifically, ChatGPT can provide assistance in solving programming errors by
offering debugging help, error prediction, and error explanation, but
currently it is only suitable to analyze and understand code snippets
(Surameery and Shakor, 2023). In addition, similar viewpoints are present in
(Castelvecchi, 2022), which implies that ChatGPT has an impact on the entire
software industry. While it cannot currently replace programmers, it is
capable of generating short computer programs with limited execution.
Moreover, a specific programming test about ChatGPT’s Python programming
ability is conducted in (Amos, 2023). Furthermore, ChatGPT’s programming
ability is tested from two perspectives: the first is from the perspective of
a programming novice, relying solely on his/her own programming skills; the
second is by providing specific programming prompts to it (Amos, 2023).
However, the test results of the former are disappointing because the program
does not run as expected by the author. In the latter approach, the author
provides ChatGPT with more prompts and divides the programming task into
separate functions for it to generate, which yields an expected generation
(Amos, 2023). Overall, it can be observed that ChatGPT currently faces some
difficulties in generating long texts and cannot be used as a standalone
programmer. However, if provided with more guidance and tasked with generating
relatively shorter text, its performance is excellent.

Management tool. With advanced language understanding and generation
capabilities, ChatGPT has rapidly become an important management tool for
organizations in various industries, including the construction industry,
product management, and libraries (Prieto et al., 2023; Zhao, 2023; Verma,
2023). The construction industry requires a significant amount of repetitive
and time-consuming tasks, such as the need for strict supervision and
management of construction progress. At this point, ChatGPT can be used to
generate a construction schedule based on the project details provided by
users, reducing labor costs and improving construction efficiency in the
construction industry (Prieto et al., 2023). In addition to its application in
the construction industry, it can also be applied to product management.
ChatGPT can be integrated into almost every step of the product management
process, such as getting early ideas on marketing, writing product
requirements documents, designing the product, analyzing the feedback from
users and even creating a draft for go-to-market (Zhao, 2023). Another example
is that it has the potential to significantly impact traditional libraries as
a library management tool. Given ChatGPT’s ability to manage books and analyze
data, customers can quickly obtain answers to their questions, enhancing the
user experience. Furthermore, library staff can focus on more complex tasks
and provide more efficient service to customers (Verma, 2023).

Miscellaneous applications. In addition to the fields indicated above, ChatGPT
can be utilized in financial, legal advising, societal analysis, and
accounting. ChatGPT’s potential for upgrading an existing NLP-based financial
application is explored (Zaremba and Demir, 2023). The performance of ChatGPT
as an expert legal advice lawyer is access (Macey-Dare, 2023; Bishop, 2023).
ChatGPT, in particular, gives a deep and thought-provoking analysis of the
Libor-rigging affair, as well as the implications of the current Connolly and
Black case for Tom Hayes’ conviction (Macey-Dare, 2023). Multiple works
(Haluza and Jungwirth, 2023; Jungwirth and Haluza, 2023) have been conducted
to examine the potential of ChatGPT for societal analysis, focusing not just
on the 10 social megatrends (Haluza and Jungwirth, 2023) but also on
geopolitical conflicts (Jungwirth and Haluza, 2023), and the results show
ChatGPT can have a positive impact for this application. (Street and Wilck,
2023; Alshurafat, 2023) provide guidance on successfully and effectively
deploying ChatGPT in the field of accounting.

##  5\. Challenges

###  5.1. Technical limitations

Despite its powerful capabilities, ChatGPT has its own drawbacks, which are
officially recognized by the OpenAI team. Numerous works (Lipenkova, 2023;
Cherian et al., 2022; Blog, 2023; Zhuo et al., 2023; Borji, 2023; Hanna, 2023;
Saghafian, 2023) have been conducted to demonstrate its limitations, which are
summarized as follows:

Incorrect. ChatGPT sometimes generates wrong or meaningless answers that
appear to be reasonable, which is like talking nonsense in a serious way
(Borji, 2023). In other words, the answer provided by ChatGPT is not always
reliable (Zhuo et al., 2023; Blog, 2023; Borji, 2023). As recognized by
OpenAI, this issue is challenging, and a major reason is that the current
model training depends on supervised training and reinforcement learning to
align the language model with instructions. As a result, the model mimics the
human demonstrator to be plausible-sounding but often at the cost of
correctness. The factual error-related issues have been mitigated in the
ChatGPT plus version, but this problem still exists (OpenAI, 2023a).

Illogical. It is noted in (Hanna, 2023; Saghafian, 2023; Borji, 2023) that
ChatGPT’s logic reasoning capability still needs improvement. Since ChatGPT
lacks rational human thinking, it can neither “think” nor “reason” and thus
failed to pass the Turing test (Hanna, 2023). ChatGPT is merely a
sophisticated statistical model, unable to understand its own or the other’s
words and answer in-depth questions (Saghafian, 2023). In addition, ChatGPT
lacks a “world model” to perform spatial, temporal, or physical inferences, or
to predict and explain human behaviors and psychological processes (Borji,
2023), and is also limited in mathematics and arithmetic, unable to solve
difficult mathematical problems or riddles, or even possibly get inaccurate
results in some simple computation tasks (Borji, 2023).

Inconsistent. ChatGPT can generate two different outputs when the model is fed
with the same prompt input, which suggests that ChatGPT has the limitation of
being inconsistent. Moreover, ChatGPT is highly sensitive to the input prompt,
which motivates a group of researchers investigating prompt engineering. A
good prompt can improve the query efficiency for systematic review literature
search (Wang et al., 2023b). The efficiency of automating software development
tasks can be further improved by utilizing prompt patterns such as effective
catalogues and guidance about software development tasks (White et al., 2023a,
b). Despite the progress in discovering better prompts for ChatGPT, the fact
that simply changing the prompt can yield significantly different outputs has
an implication that ChatGPT needs to improve its robustness.

Unconscious. ChatGPT does not possess self-awareness (Borji, 2023), although
it can answer various questions and generate seemingly related and coherent
text, it does not have consciousness, self-awareness, emotions, or any
subjective experience. For example, ChatGPT can understand and create humour,
but it cannot experience emotions or subjective experiences (Borji, 2023).
There is no widely accepted definition of self-awareness yet, nor reliable
test methods. Some researchers suggest inferring self-awareness from certain
behavior or activity patterns, while others believe it is a subjective
experience that cannot be objectively measured (Borji, 2023). It is still
unclear whether machines truly possess or can only simulate self-awareness.

###  5.2. Misuse cases

The powerful capabilities of ChatGPT can be misused in numerous scenarios.
Here, we summarize its misuse cases, which are summarized as follows:

Plagiarism and misconduct. The most likely misuse of ChatGPT is academic and
writing plagiarism(Cotton et al., 2023; Shiri, 2023; Ali et al., 2023;
Ventayen, 2023). Students may use the content generated by ChatGPT to pass
exams and submit term papers. Researchers may use the content generated by
ChatGPT to submit papers and conceal the use of ChatGPT (Cotton et al., 2023).
Many schools have already prohibited the use of ChatGPT, and the emergence of
such tools is disruptive to the current education system and the criteria for
evaluating student performance (Shiri, 2023). If students use ChatGPT and hide
it, it is unfair to those who do not use ChatGPT. This behavior undermines the
goals of higher education, undermines the school’s education of students, and
may ultimately lead to the devaluation of degrees.

Over reliance. The use of ChatGPT by students and researchers to generate
ideas might lead to more terrifying issues, that is, their over-dependence on
the model and abandoning their independent thinking(Marchandot et al.,
2023)(Shiri, 2023)(Ali et al., 2023)(Pfeffer et al., 2023), which not only
means the simple issue of writing plagiarism, but a more serious one. Although
ChatGPT can generate constructive answers according to the questions asked,
just like search engines, but more powerfully. This effortless generation of
ideas or guidance may gradually weaken the ability of critical thinking and
independent thinking (Shiri, 2023). In order to ensure that students and
researchers do not neglect their own thinking ability, some measures can be
taken, such as providing more comprehensive discussion opportunities for
students and researchers to really think about the problems; in addition,
basic methods of critical thinking can be taught in class, so that students
can learn to think about problems rather than simply using ChatGPT (Pfeffer et
al., 2023).

Improper content. ChatGPT may be misused to spread false information and
generate toxic content that can cause harm to society. For example, ChatGPT
can be abused to generate pornographic, vulgar, and violent content (Dash and
Sharma, 2023), which can harm individuals and society. Hackers can use
ChatGPT’s programming capabilities to create malicious software (Dash and
Sharma, 2023), such as viruses or Trojans, for network attacks, data theft, or
attempts to control other computer systems, which can cause serious harm to
other network users. Finally, trolls may use specific prompts to induce
ChatGPT to generate harmful content as a way to attack others (Zhuo et al.,
2023). Moreover, ChatGPT does not receive any human review when generating the
content, which makes it difficult to hold someone accountable when
inappropriate content appears in the output (Ali et al., 2023).

False dissemination. ChatGPT may generate false information, thus leading to
the problem of wrong information dissemination (Zhuo et al., 2023; Borji,
2023). For example, ChatGPT may be exploited to generate a large number of
fabricated articles that appear on blogs, news, newspapers, or the internet
that look indistinguishable from other articles but are actually false.
Disseminating such forgeries not only harms the public interest but also
disrupts the network environment (Dash and Sharma, 2023). Microsoft has added
ChatGPT to its search engine Bing, which will accelerate the speed of wrong
information spreading on the Internet. If not controlled, the rapid spread of
wrong information on the Internet will have disastrous consequences for public
information security (De Angelis et al., 2023). Therefore, a new public
information epidemic threat “Artificial Intelligence Information Epidemic” is
proposed (De Angelis et al., 2023). Meanwhile, it calls on the public to be
aware of the accuracy of information when using large-scale language models to
prevent the spread of wrong information, which is essential for improving the
reliability of public information.

###  5.3. Ethical concerns

With the wide use of ChatGPT, there is increasing attention to the underlying
ethical concerns. Here, we summarize the ethical concerns behind, which are
summarized as follows:

Bias. Due to the fact that ChatGPT is trained on large amounts of data
generated by humans and is adjusted according to human feedback, the generated
content is influenced by human authorities and thus has biases (Azaria, 2023).
For example, ChatGPT has been found to have political biases, when creating an
Irish limerick (McGee, 2023), the contents of the limerick tended to support
liberal politicians rather than conservative politicians. Furthermore, ChatGPT
has a left-wing liberal ideological bias when reviewing the importance of
political elections in democratic countries (Hartmann et al., 2023). The
biased data generated by ChatGPT can influence students during the process of
education, thus magnifying the phenomenon of bias in society (Ali et al.,
2023; Marchandot et al., 2023).

Privacy. ChatGPT may infringe on personal privacy in both its training process
and user utilization process. During the training process, ChatGPT collects a
large amount of data from the Internet which may contain sensitive personal
privacy and confidential information, and the model may be maliciously led to
leak personal privacy or confidential information, or even be maliciously
guided to create false or misleading content, thus affecting public opinion or
personal reputation. During the user utilization process (Ali et al., 2023;
Pfeffer et al., 2023), users may unintentionally disclose their own
information to meet their own needs, such as personal preferences, and chat
records. Thus, such information may bring adverse effects to users if obtained
by criminals.

Fairness. ChatGPT also raises concerns about fairness. For example, in
academics, it is argued in (Liebrenz et al., 2023) that ChatGPT can
democratize the dissemination of knowledge, as it can be used in multiple
languages, thus bypassing the requirement of the English language. On the
other hand, the free use of ChatGPT is only temporary, and the fee charged for
ChatGPT will exacerbate the inequality in the academic field internationally.
Educational institutions in low-income and middle-income countries may not be
able to afford it, thus exacerbating the existing gap in knowledge
dissemination and academic publishing (Liebrenz et al., 2023; Pfeffer et al.,
2023).

Transparency. So far, how large language models like GPTs work to generate the
relevant responses is still unclear (Larsson and Heintz, 2020; Wischmeyer,
2020), which renders the decision process of ChatGPT lack transparency. The
lack of transparency makes it difficult for the user to have fine-grained
control of the generated content, and is especially problematic when the
generated content is toxic. More worrisome is that the company OpenAI has
deviates from its original non-profit goal to pursue a business interest,
which makes it less reluctant to reveal the underlying technical details of
its recent progress. For example, the recently released GPT-4 technical report
(OpenAI, 2023a) mainly demonstrates its superiority over the previous model
families, while providing no technical details on how these are achieved.

###  5.4. Regulation policy

Numerous scholars have discussed how to make regulations on the capabilities
and impacts of ChatGPT, and the most frequently discussed topics are listed in
the following paragraphs.

Misuse prevention. A major concern for the misuse of ChatGPT is that it might
damage academic integrity. Directly prohibiting the use of ChatGPT in academic
institutions is not recommended (Hargreaves, 2023). To this end, some propose
to cancel assignments based on article writing and seek alternative test forms
to stop students from abusing ChatGPT (Shiri, 2023; Williams, 2023). It is
also possible to enrich student courses, such as adding thinking exercises
courses, or teaching students how to use ChatGPT correctly (Pfeffer et al.,
2023). Another approach is to develop AI content detectors. Detecting whether
ChatGPT generates a piece of content or not is an arduous task, even for
professionals with master’s or PhD backgrounds who are unable to correctly
identify whether the content is generated by ChatGPT (Hisan and Amri, 2023;
Pfeffer et al., 2023). Many developers use software to detect whether the
content is AI-generated (Khalil and Er, 2023; Zhou et al., 2022). ChatZero
developed by Edward Tian, a student from the Department of Computer Science at
Princeton University, measures the complexity of the input text to detect
whether it is generated by ChatGPT or created by humans, and provides
plagiarism scores to list out the plagiarism possibilities in detail (Shiri,
2023). ChatGPT is used to detect whether the content is generated by itself,
and it has been proven to perform better than traditional plagiarism detection
tools (Khalil and Er, 2023).

Co-authorship. Recently, multiple articles (Kung et al., 2023; O’Connor et
al., 2022; Transformer and Zhavoronkov, 2022; Transformer et al., 2022) have
listed ChatGPT as co-authors, sparking debate on whether ChatGPT can be listed
as a co-author among journal editors, researchers, and publishers (McKee and
Noever, 2022; Polonsky and Rotman, 2023; Ueda and Yamada, 2023; da Silva,
2023). Those who believe that ChatGPT should not be listed as an author argue
that it does not meet the four criteria for authorship set by the
International Committee of Medical Journal Editors (ICMJE) (Yeo-Teh and Tang,
2023). Moreover, it is highlighted in (Thorp, 2023) that ChatGPT is not
creative or responsible, and its text may involve plagiarism and ethical
issues, which might break the standards of content originality and quality.
However, some argue that AI tools such as ChatGPT have the capacity or will
have the capacity to meet the ICMJE authorship criteria and thus ChatGPT is
qualified to be a co-author (Polonsky and Rotman, 2023). Regarding this issue,
Nature (Shiri, 2023) has clearly stated that large language models like
ChatGPT do not meet the criteria for authorship and require authors to
explicitly state how ChatGPT was used in the writing. An interesting point has
been made in (McKee and Noever, 2022) that the debate over whether AI can be
considered a “co-author” is unnecessary because the role of authors in
traditional academic writing might have already changed when the debate
arises.

Copyright. Does the content generated by ChatGPT have a copyright? The content
generated solely by ChatGPT is not protected by copyright. According to the
rules of the US Copyright Office, only human creations can be protected by
copyright. If there is no creative input or interference from a human author,
a machine or mechanical program that runs randomly or automatically is not
protected by copyright(Chesterman, 2023).

##  6\. Outlook: Towards AGI

###  6.1. Technology aspect

In this booming generative AI era, there are numerous AIGC tools for various
generative tasks, including text-to-text (Raffel et al., 2020; Kale and
Rastogi, 2020; Narang et al., 2020; Xue et al., 2020; Berabi et al., 2021),
text-to-image (Mansimov et al., 2016; Tao et al., 2023; Reed et al., 2016;
Zhang et al., 2017; Xu et al., 2018), image captioning (Hossain et al., 2019;
Vinyals et al., 2016; Yao et al., 2017), text-to-speech (Taylor, 2009; Klatt,
1987; Ren et al., 2019), speech recognition (Li et al., 2022; Wang et al.,
2019; Park et al., 2019; Schneider et al., 2019; Liu et al., 2023a), video
generation (Maulana, 2023; Molad et al., 2023; Ho et al., 2022; Yang et al.,
2022), 3D generation (Mittal et al., 2022; Hoogeboom et al., 2022), etc.
Despite its impressive capabilities, it is noted in (Gozalo-Brizuela and
Garrido-Merchan, 2023) that ChatGPT is not all you need for generative AI.
From the input and output perspective, ChatGPT mainly excels at text-to-text
tasks. With the underlying language model evolving from GPT-3.5 to GPT-4,
ChatGPT in its plus version increases its modality on the input side.
Specifically, it can optionally take an image as the input, however, it can
still not handle video or other data modalities. On the output side, GPT-4 is
still limited to generating text, which makes it far from a general-purpose
AIGC tool. Many people are wondering about what next-generation GPT might
achieve (Aydın and Karaarslan, 2023; Bubeck et al., 2023). A highly likely
scenario is that ChatGPT might take a path toward general-purpose AIGC, which
will be a significant milestone to realize artificial general intelligence
(AGI) (Bubeck et al., 2023).

A naive way to realize such a general-purpose AIGC is to integrate various
AIGC tools into a shared agent in a parallel manner. A major drawback of this
naive approach is that there is no interaction among different AIGC tasks.
After reviewing numerous articles, we conjecture that there might be two road-
maps for bridging and pushing ChatGPT toward AGI. As such, we advocate a
common landscape to achieve the interconnection between diversified AIGC
models.

Figure 6. Roadmaps for bridging the gap between ChatGPT and AGI.

Road-map 1: combining ChatGPT with other AIGC tools. As discussed above,
current ChatGPT mainly excels in text-to-text tasks. A possible road map for
bridging the gap with general-purpose AIGC is to combine ChatGPT with other
AIGC tools. Let’s take text-to-image tasks as an example: the current chatGPT
(GPT-3) cannot be directly used to generate images. Existing text-to-image
tools, like DALL-E 2 (Ramesh et al., 2022) or stable diffusion (Rombach et
al., 2022), mainly focus on the mapping from a text description to a plausible
image, while lacking the capability to understanding complex instruction. By
contrast, ChatGPT is an expert in instruction understanding. Therefore,
combining ChatGPT with existing text-to-image AIGC tools can help generate
images with delicate details. A concrete example is shown in (Bubeck et al.,
2023) to utilize ChatGPT to generate an SVG code (Eisenberg and Bellamy-Royds,
2014) or TikZ code (Ellis, 2017) to draw a sketch for facilitating image
generation under detailed instructions.

Road-map 2: All-in-one strategy. The above road map renders ChatGPT mainly as
a master of language understanding by exploiting the downstream AIGC tools as
slaves. Such a combination strategy leverages advantages from both sides but
with the information flow mainly from ChatGPT to the downstream AIGC tools.
Moreover, there is still no interaction between different AIGC tasks. To this
end, another road map might come to solve all AIGC tasks within the ChatGPT
and excludes the dependence on the downstream AIGC tools. Similarly, we
consider music generation as an everyday use case. For example, a user can
instruct the ChatGPT with prompts like “Can you generate a music clip to match
the input image”, and ChatGPT is supposed to synthesize such a desired music
clip. Such an input image is optional, depending on the task. For example, a
simple corresponding instruction prompt is sufficient if the task requires
generating music beneficial for sleep. Such an all-in-one strategy might the
model training a challenging task. Moreover, the inference speed might be
another hurdle, for which pathways (Chowdhery et al., 2022) might be a
solution.

Another evolving path might lie between road maps #1 and #2. In other words,
road map #1 might be a more applicable solution in the early stages. With the
technology advancing, ChatGPT is expected to master more and more AIGC tasks,
excluding the dependence on external tools gradually.

###  6.2. Beyond technology

In the above, we present an outlook on the technology path that ChatGPT might
take towards the ultimate goal of AGI. Here, we further discuss its potential
impact on mankind from the perspective of how AGI might compete with mankind.
Specifically, we focus on two aspects: job and consciousness.

Can AGI replace high-wage jobs? Multiple works have performed a comprehensive
analysis of the influence of ChatGPT on the job market (Zarifhonarvar, 2023;
Eloundou et al., 2023; Felten et al., 2023). According to the statistics in
(Zarifhonarvar, 2023), 32.8% of jobs are fully affected and 36.5% may be
partially affected. Meanwhile, it points out that the jobs that will be fully
impacted are those that involve doing routine tasks, while the jobs that will
be partially affected are those that can be partially replaced by AI
technologies (Zarifhonarvar, 2023). OpenAI has also investigated large
language models like GPTs might affect occupations (Eloundou et al., 2023).
Their findings show that at least 10% of tasks for 80% of the US workforce and
at least 50% of tasks for 19% of workers will be impacted. It is worth noting
that the advent of new technology will inevitably replace some types of jobs.
However, what makes AGI different is its potentially greater influence on
high-end jobs than on low-end ones. This outlook is partially supported by the
findings in (Eloundou et al., 2023; Zarifhonarvar, 2023) that high-wage jobs
tend to have a higher risk of being replaced by AGI, for which lawyer is a
representative occupation. The reason that AGI poses a higher threat to that
high-wage jobs is that most current high-wage jobs typically require
professional expertise or creative output, which conventional AI cannot
replace.

Can AGI have its own intention and harm mankind? In numerous fiction movies,
an AI agent can have its own consciousness with its own intention. Such a
human-level AI agent used to be far from reality, and a major reason is that
other AI agents cannot make inferences. There is evidence that ChatGPT has
developed such a capability, the reason for which is not fully clear, as
acknowledged by Altman (founder of OpenAI) in his recent interview with Lex
Fridman. Moreover, Altman also mentioned the possibility of AI harming
mankind. Due to such concerns, very recently, Future of Life Institute has
called on all AI labs to pause giant AI experiments on the training of AI
systems more powerful than GPT-4. and the number of signing this public letter
has exceeded a thousand, including Yoshua Bengio, Stuart Russel, Elon Musk,
etc. It is highlighted at the beginning of the letter that (we quote) “AI
systems with human-competitive intelligence can pose profound risks to society
and humanity”, which shows deep concerns about the advent of AGI. The deepest
concern lies in the risk that AGI might outsmart and eventually replace us as
well as destroy mankind’s civilization. However, not everyone agrees with its
premise. For example, Yan Lecun is one of those who publicly disclose their
attitude. It remains unclear how such a controversial movement might affect
the future of pushing ChatGPT (or other products with similar functions)
towards AGI. We hope our discussion helps raise awareness of the concerns
surrounding AGI.

##  7\. Conclusion

This work conducts a complete survey on ChatGPT in the era of AIGC. First, we
summarize its underlying technology that ranges from transformer architecture
and autoregressive pretraining to the technology path of GPT models. Second,
we focus on the applications of ChatGPT in various fields, including
scientific writing, educational technology, medical applications, etc. Third,
we discuss the challenges faced by ChatGPT, including technical limitations,
misuse cases, ethical concerns and regulation policies. Finally, we present an
outlook on the technology road-maps that ChatGPT might take to evolve toward
GAI as well as how AGI might impact mankind. We hope our survey provides a
quick yet comprehensive understanding of ChatGPT to readers and inspires more
discussion on AGI.

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