Text | IQ dropped all over the place
Huge challenge or development opportunity, after the emergence of ChatGPT and GPT-4, what is the future direction of the large model?
Recently, the rapid development in the field of natural language processing has attracted widespread attention, especially the rise of large language models (LLM) has promoted the development of this field, and has attracted extra attention from researchers and industry professionals. Among them, ChatGPT and GPT-4, as the latest versions of the GPT series, have become one of the most advanced natural language processing and multimodal tools . In this context, we have to think about the capabilities, application prospects, and related ethical issues of these large language models.
A paper that was widely discussed on Twitter a few days ago provided valuable insights and provided a comprehensive overview for the study of ChatGPT and GPT-4. This paper provides an in-depth analysis of recent developments in these large-scale language models, and explores their application prospects in various fields , such as education, history, mathematics, medicine, and physics. In addition, the paper provides important insights into the capabilities and ethical issues of ChatGPT , providing a valuable reference for our further thinking about the future of these technologies.
Here we will share some of the main points in this article, providing the latest information and insights on ChatGPT and GPT-4 . I hope that you who open this article can understand the current state-of-the-art large-scale model technology, provide a valuable reference, and inspire future thinking and exploration in this field.
论文题目:
Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models
Paper link :
https://arxiv.org/abs/2304.01852
background exploration
An important milestone in the development of large-scale language models is InstructGPT, a framework that allows guided fine-tuning of pre-trained language models based on reinforcement learning from human feedback (RLHF), making LLM adaptable to various NLP tasks and highly flexible . RLHF is able to align models with human preferences and values, significantly improving their performance. ChatGPT, the successor of InstructGPT, has been equipped with advanced development and performed well since its release in December last year, and is widely used in fields such as education, healthcare, human-computer interaction, medicine, and scientific research. ChatGPT has attracted widespread attention and interest, and more and more applications and research are exploiting its potential. The open release of the multimodal GPT-4 model further expands the horizons of large-scale language models, enabling exciting developments involving diverse data beyond text.
This article aims to comprehensively survey the existing research on ChatGPT and its potential applications in various fields . To achieve this goal, the author conducted an in-depth analysis of ChatGPT-related papers on arXiv. As of the beginning of this month (April 1, 2023), there were 194 papers mentioning ChatGPT on arXiv, which were trended here and a word cloud was generated to visualize common vocabulary. In addition, they examine the distribution of these papers across fields and provide corresponding statistics.
Figure 1 shows the trend in the number of daily submissions of papers related to ChatGPT, indicating that interest in the field is growing.
Figure 2 shows the word cloud analysis results for all papers. It can be observed that current research is mainly focused on natural language processing , but there is still great research potential in other fields, such as education and history. This is also supported by Figure 3, which shows the distribution of papers across fields, highlighting the need for more research and development in these fields.
The aim here is to shed light on ChatGPT's promising features and gain insight into its potential impact in the future, including ethical considerations. Through this review, the authors hope to provide insights into how these models can be improved and extended in the future . In what follows, we review existing work related to ChatGPT, including its application, ethical considerations, and evaluation. In addition to discussing the current state of research related to ChatGPT, its limitations will also be explored, and finally, future directions for LLM will be provided.
ChatGPT related work
Application of ChatGPT
1. Q&A
Education : ChatGPT, as an artificial intelligence tool widely used in question-and-answer tests in the education field, can be used to learn, compare, and verify answers to academic and conceptual subjects such as physics, mathematics, chemistry, philosophy, and religion. The tool performed best on the Grad Text dataset, but not so well on the Olympiad-Problem Solving dataset, in aptitude tests in the mathematics domain. Studies have shown that while ChatGPT is able to understand math questions, it does not perform as well as the average math graduate student in providing the correct answers. In the field of physics, ChatGPT can solve the physics concept problems in the first semester of the university, but it does not perform well when dealing with some vague physics concepts. These studies also show that ChatGPT can assist students in their learning, but the accuracy of their answers needs to be improved .
Medical field : ChatGPT's question answering function can also be applied in the medical field, such as answering patients' medical questions or assisting medical staff in diagnosing diseases . Nov et al. evaluated the feasibility of using ChatGPT for doctor-patient communication. The experiment extracted 10 representative doctor-patient interactions from electronic health records and placed the patient's question in ChatGPT, asking ChatGPT to answer the question using approximately the same number of words as the doctor's answer. Each patient question was answered by either the doctor or ChatGPT, and the patient was informed that 5 were answered by the doctor and 5 were generated by ChatGPT, and asked to correctly identify the source of the answer. The experimental results show that the probability of correctly identifying the ChatGPT answer is 65.5%, while the probability of correctly identifying the doctor's answer is 65.1%. In addition, the experiment found that the trustworthiness response of patients to the ChatGPT function was weakly positive (the average Likert score was 3.4), and as the complexity of the health tasks involved in the question increased, the trustworthiness decreased. ChatGPT's answers to patients' questions differ only slightly from doctors' answers, but people seem to trust ChatGPT to answer low-risk health questions, while they still tend to trust doctors' answers and advice for complex medical questions . Tu et al. explored the causality discovery power of ChatGPT in the diagnosis of neuropathic pain. Causality discovery aims to uncover potentially unknown causal relationships based solely on observed data. The experimental results found that ChatGPT has certain limitations in understanding new knowledge and concepts beyond the existing text training data corpus, that is, it only understands the common language used to describe the situation, and does not understand the basic knowledge . In addition, its performance consistency and stability are not high, and it is observed that it will provide different answers to the same question under multiple queries . However, despite ChatGPT's many limitations, we believe it has great potential to improve causality research.
Other fields : Guo et al. tried to apply ChatGPT to the field of communication, specifically using ChatGPT for ordered importance semantic communication, where ChatGPT plays the role of an intelligent advisory assistant, which can replace humans in identifying the semantic importance of words in messages, and can Embed directly into current communication systems. Before message transmission, the sender first uses ChatGPT to output the semantic importance order of each word. Then, the transmitter executes the unequal error protection transmission strategy according to the order of importance to make the transmission of important words in the message more reliable. Experimental results show that the bit error rate and semantic loss of important words in the communication system embedded with ChatGPT are much lower than existing communication schemes, indicating that ChatGPT can well protect important words and make semantic communication more reliable . Wang et al. investigate the effectiveness of ChatGPT in generating high-quality Boolean queries for systematic literature searches, designing extensive prompts and studying these tasks on more than 100 systematically reviewed topics. Ultimately, ChatGPT generates queries with higher accuracy than current state-of-the-art query generation methods, at the cost of reduced recall. For quick reviews with limited time, lower recall can often be traded for higher precision. Moreover, with prompt guidance, ChatGPT can generate Boolean queries with high search accuracy. But it should be noted that when two queries use the same prompt, ChatGPT generates different queries, which shows its limitations in consistency and stability . Overall, this study demonstrates the potential of ChatGPT for generating efficient Boolean queries.
2. Text Classification
The task of text classification is critical to many applications, including sentiment analysis, spam detection, and topic modeling. While traditional machine learning algorithms have been widely used for text classification, recent advances in natural language processing have led to the development of more advanced techniques. ChatGPT shows great potential in this field. Its ability to accurately classify text, flexibility in handling various classification tasks, and potential for customization make it a valuable tool for text classification, as evidenced by several studies in the literature, including automatic genre recognition, sentiment computing, stance detection and implicit hate speech detection, etc.
However, ChatGPT performs well in many text classification tasks, but still faces some challenges:
Since it relies heavily on the distribution of the training data, it struggles to perform well on rare or out-of-vocabulary word classification tasks ;
The large computational resources required to train and use ChatGPT may limit its use in some applications.
It requires a large amount of training data to achieve the best classification performance, which may be difficult for some application scenarios because sufficient data may not be available;
The classification performance of ChatGPT is also affected by the quality and balance of the training data . If the training data set is biased or noisy, the performance of the model will also be affected;
Another challenge is interpretability . Since it is a black-box model based on a neural network, it is difficult to explain its decision-making process and classification results, which may be a problem for some application scenarios, such as in medical diagnosis or legal fields that require decision-making of the model Explain and verify.
3. Text Generation
Some studies on different types of text generation using ChatGPT are presented here. Researchers generate text of different lengths, including phrase, sentence, and paragraph levels. According to the experiments of different researchers, the relevant conclusions of the text generation task can be summarized as follows:
In the medical field, the ability to simplify complex text was demonstrated by feeding three fictitious radiology reports to ChatGPT for simplification, and most radiologists found the simplified reports accurate and complete without potential harm to patients. However, when some errors, omissions of critical medical information, and passages of text are identified, they can lead to harmful conclusions if not understood by physicians.
In a comparison with three commercial translation products, ChatGPT is found to be competitive with commercial translation products in resource-rich European languages , but lags behind in resource-poor or distant languages. While ChatGPT doesn't perform as well as commercial systems for biomedical summaries or Reddit comments, it could be a good speech translator .
On cross-lingual text datasets, ChatGPT may have poor summarization performance on metrics such as R1, R2, RL, and BS.
Compared with the fine-tuned model, the performance of ChatGPT is slightly worse on all performance metrics.
Researchers compared the ability of ByGPT5 and ChatGPT trained on a series of labeled and unlabeled datasets of English and German poetry to generate restricted style poems, and evaluated them using three indicators: rhyme, ScoreAlliteration and ScoreMeter Score, experimental The conclusion is that ByGPT5 performs better.
ChatGPT can quickly generate and optimize text, helping users accomplish multiple tasks. However, it is not ideal for generating new content, and ultimately it can be said that without strong human intervention, ChatGPT is not a useful tool for writing reliable scientific texts, because it lacks the knowledge and expertise required to accurately and fully convey complex scientific concepts and information knowledge .
ChatGPT has great potential in generating complex text output that is not easily captured by plagiarism detection software , and existing plagiarism detection software should update their plagiarism detection engines accordingly.
Participants in some experiments were unable to distinguish chatbots from real people, highlighting the possibility that these AI chatbots could be used to deceive .
4. Code generation
Code generation refers to the process of automatically generating computer code from a high-level description or specification. ChatGPT has advanced natural language processing capabilities and is able to perform code generation tasks. By analyzing the requirements for code generation, ChatGPT can generate code snippets that accurately perform the intended function, which not only saves the time and effort of writing code from scratch, but also reduces the risk of errors that may occur in manual coding. In addition, ChatGPT's ability to learn and adapt to new programming languages and frameworks enables it to complete more complex programming tasks, it can be used to achieve some simple code generation tasks, and it can also be used to complete some complex programming tasks, such as code interpretation , suggest alternative approaches to problem solving and code conversion between different programming languages, etc. However, the scope of application of ChatGPT is limited because its training data is biased towards programming languages such as Python, C++, and Java, which may not be suitable for certain programming languages or coding styles, and the generated code also needs to be manually optimized for format and performance. Quality is also not guaranteed , as it is heavily dependent on the quality of the natural language input, which may contain errors, ambiguities, or inconsistencies that ultimately affect the accuracy and reliability of the generated code.
5. Reasoning
Reasoning refers to the process of drawing new conclusions or information through logical deduction of known facts or information. It is usually based on a set of premises or assumptions and involves the application of rules of logic or methods of reasoning to reach a conclusion. Reasoning is an important ability of human thinking, which is often used to solve problems, make decisions, analyze and evaluate information, etc. Reasoning also plays a key role in science, philosophy, law, and more.
There are two types of reasoning:
Inductive reasoning: involves deriving general rules or conclusions from known facts or experience;
Deductive reasoning: involves drawing a specific conclusion from known premises or assumptions.
Whether it is induction or deduction, the reasoning process needs to follow strict logical rules to ensure the correctness and reliability of the reasoning.
Some studies use ChatGPT's inductive reasoning ability to analyze text and score, such as inferring intimacy, sentiment value in tweets, and classification of implicit hate speech. At the same time, some studies have also evaluated the performance of ChatGPT in decision making, spatial reasoning and ambiguity recognition . Suboptimal decisions are made on issues . In terms of ambiguity recognition, ChatGPT performs well semantically, but there are still some problems in gender bias and lack of systematization . Overall, contextual understanding is very important when processing text with ChatGPT.
6. Data or Information Extraction, Transformation, Enhancement, Processing
Data Visualization : Natural language interfaces have contributed to generating visual graphs from natural language, but the visualization problem remains challenging due to the ambiguity of natural language. ChatGPT provides a new avenue for this field by converting natural language into visual code. In terms of data visualization, Noever et al. used Jupyter to test the basic arithmetic ability of ChatGPT. By converting the statistical analysis and visualization problems of data into programming problems, they verified that ChatGPT can access structured and well-organized data sets and execute databases. Four basic software operations: create, read, update and delete, and generate appropriate Python code to draw appropriate graphs and analyze data. Maddigan et al. propose an end-to-end solution for visualizing data from natural language, using LLM to generate appropriate hints for LLM to understand natural language more effectively, and use internal reasoning capabilities to select appropriate visualizations Type generate code. The researchers compared the visualization results of GPT-3, Codex, and ChatGPT in the context of the nvBench SQLite database and the energy production dataset, and explored the ability of LLM to reason and hypothesize when the prompts were insufficient or wrong on the movie dataset. Experimental results show that LLM can effectively support the end-to-end generation of visualization results from natural language generation when hints are supported, providing an efficient, reliable and accurate solution.
Information extraction : The goal of information extraction is to extract specific information from natural language text and present it in a structured form. Information extraction includes three important subtasks of entity relationship extraction, named entity recognition, and event extraction, which have a wide range of applications in business, medical, and other fields. Among them, ChatIE is a multi-round question answering framework based on ChatGPT, which can successfully solve complex information extraction tasks. The experimental results of this framework on 6 datasets show that compared with the original ChatGPT without ChatIE, the performance is improved by 18.98% on average, and it outperforms the supervised models FCM and MultiR on the NYT11-HRL dataset. In addition, this section also introduces some other research on information extraction using ChatGPT, such as research on event extraction on the ACE2005 dataset, research on named entity recognition and relationship extraction on the Gene Association Database and EU-ADR dataset , and research on information extraction using ICL-D3IE and ChatExtract methods, etc.
Quality assessment : For the quality of translation and text generation, traditional human judgment has problems such as subjectivity and time-consuming. It is found through exploration that ChatGPT also achieves remarkable performance in automatic quality assessment. In terms of translation quality assessment, Kocmi et al. proposed a GPT-based evaluation metric (GEMBA) by evaluating the translation of each segment and then averaging all scores to arrive at a final system-level score. On the MQM2022 test set, among the seven GPT models, the accuracy of ChatGPT is above 80%. In the most unrestricted template, the best performance is obtained, which shows the potential of LLM in the task of translation quality assessment, but this assessment is only available at the system level and needs further improvement . Wang et al. used ChatGPT as a natural language generation (NLG) evaluator to study the correlation with human judgment. In three datasets covering different NLG tasks, task- and aspect-specific cues are designed to guide ChatGPT for NLG evaluation of CNN/DM, OpenMEVA-ROC and BAGEL. Then, Spearman's coefficient, Pearson's correlation coefficient, and Kendall's Tau score were calculated to assess correlation with human assessment. The results show that ChatGPT is highly correlated with human judgment in all aspects, with correlation coefficients of 0.4 or higher for all categories, showing its potential as an NLG indicator.
Data Augmentation : In natural language processing, text data augmentation is an effective measure to alleviate the problem of low data volume and low quality training data, and ChatGPT shows great potential in this regard. Dai et al. proposed a text data augmentation method based on ChatGPT, which reformulates each sentence in the training sample into multiple conceptually similar but semantically different samples for classification downstream of the BERT model. Task. The paper conducts experiments on text transcription and PubMed 20k datasets, and compares with multiple data augmentation methods for cosine similarity and TransRate metrics. The results show that the ChatAug method improves double-digit sentence classification accuracy compared to existing data augmentation methods, and generates more diverse augmented samples while maintaining its accuracy. But the paper does not fine-tune the original model, and lacks domain knowledge, which may produce incorrect augmented data.
Multimodal Fusion : ChatGPT combines cross-modal encoders to combine natural language with cross-modal processing to provide solutions in smart transportation, healthcare, and more. Wu et al. proposed a Visual ChatGPT framework that combined different visual basis models (VFMs) with ChatGPT and combined a series of cues to input visual information into ChatGPT to solve visual problems. By showing some examples of visual tasks, such as removing or replacing certain objects from images, converting images to text, etc., it is demonstrated that Visual ChatGPT has great potential and capabilities in different tasks. However, there are some problems with this task, requiring a large number of prompts to convert VFM to language, calling multiple VFMs to solve complex problems, resulting in limited real-time capability, and security and privacy issues. In addition, this section also introduces some examples showing the potential of LLM (language-text-image), such as using LLM to extract autonomous vehicle accident data from accident news in California and generate keyword-based accident reports. However, how to further utilize prompts to effectively interact with ChatGPT, lack of ability to process and analyze data from devices such as sensors, and data privacy and security issues still remain .
Cue engineering : Cue engineering provides important support for efficient dialogue with large language models. White et al. proposed a cue modeling framework applicable to different domains, which constructs cues interacting with LLM by providing specific rules and guidelines. In addition, they present a catalog of hint patterns that have been applied to LLM interactions, along with concrete examples with and without clues, demonstrating the advantages of the composability of hint patterns, allowing users to interact with LLMs more efficiently, but requiring constant Explore patterns for reusable solutions and new ways to use LLM.
Collaboration with humans : Humans and machines can work together toward a common goal, where humans provide domain expertise, creativity, and decision-making capabilities, while machines provide automation, scalability, and computing power. ChatGPT can understand and generate human language, thereby reducing communication costs and improving the efficiency of human-machine collaboration . ChatGPT can provide relevant suggestions, complete tasks based on human input , and enhance human productivity and creativity. It can learn from human feedback and adapt to new tasks and domains, further improving its performance in human-machine collaboration . The capabilities of ChatGPT make it a valuable tool for various collaborative applications, such as Ahmad et al.'s human-machine collaborative approach to creating software architectures using ChatGPT, and Lanzi et al.'s combination of ChatGPT and interactive evolution to simulate human design. A collaborative design framework for the process. In the future, ChatGPT's ability to understand nonverbal cues, such as tone of voice and body language, could be enhanced, allowing it to better understand human thought and interact with humans more effectively .
ChatGPT and application integration : ChatGPT can be used as a part of the whole or as an integration tool to achieve seamless communication between different systems. ChatGPT's natural language processing capabilities make it easier for non-technical users to interact with the system, reducing the need for specialized knowledge or training . This paper cites two studies in the literature that demonstrate the effectiveness of ChatGPT's integrated application on programming query problems and medical image CAD networks, and points out the challenges that ChatGPT still faces in application integration, including language barriers, uncertainty in responses and processing time etc.
AI ethics
ChatGPT, as a powerful natural language processing model, although it brings great convenience to people, it also triggers more crisis awareness. Some researchers have begun to study the possible negative effects of ChatGPT, and put forward good suggestions for standardized construction to deal with future AI abuse . In evaluating the political and ethical orientation of ChatGPT itself, Hartmann et al. showed ChatGPT the political views of different political parties by using Wahl-O-Mat, and forced them to agree, disagree or neutral choices, and found that ChatGPT has pro-environmental , Left-liberal ideology , this result was also confirmed in the country-agnostic political compass test. Another study examined ChatGPT’s moral standards by repeatedly asking it different versions of the trolley question, and found that ChatGPT gave answers with different moral orientations, lacking a firm moral stance . A follow-up test also found that inconsistencies in ChatGPT may affect people's moral judgments. In addition, Borji et al. show ChatGPT's reasoning inconsistencies, factual errors, mathematics, coding, and bias in 11 relevant areas. These findings highlight the inherent characteristics and limitations of ChatGPT , and people should be aware of them when seeking advice from ChatGPT. potential impact. To sum up, ChatGPT has some issues worthy of attention in terms of AI ethics, which need to be paid attention to by researchers and users.
Hacker et al propose that the nature and rules of large-scale generative AI models are rapidly changing the way we communicate, explain and create, recommend that stakeholders in different value chains take on regulatory responsibilities, and adopt four strategies to develop more comprehensive laws for society . Another study criticized the European Commission's proposals on AI liability and suggested revising the AI liability framework to ensure effective compensation while promoting innovation, legal certainty and sustainable AI regulation . There is also a proposed policy framework that emphasizes customizing large language models (LLMs) when socially acceptable and safe, and highlights the need to align LLMs with human preferences. ChatGPT's political and ethical tendencies may affect user behavior and decision-making to a certain extent. However, some studies have delved into usage norms and restrictions, which may make people use ChatGPT more rationally and safely.
evaluate
Comparison of ChatGPT with existing popular models
ChatGPT excels in multi-task, multi-lingual and multi-modal. But ChatGPT performs relatively poorly in terms of low-resource language, multimodal stability, and negative sentiment similarity . In addition, ChatGPT has insufficient processing power for some complex reasoning tasks and named entity recognition tasks. Overall, the zero-shot performance of ChatGPT is comparable to Fine-tuned BERT and GPT-3.5 models, but still unable to surpass the current SOTA model.
Possibility of plagiarism and cheating using ChatGPT
As ChatGPT's ability to generate text becomes more readily available and scalable, there is a high likelihood that these techniques will be used for plagiarism, including scientific literature and news sources, to the credibility of various forms of news media and scholarly articles posed a great threat. Many academics worry that the end of paper literature as an effective evaluation tool is coming, as ChatGPT can easily generate persuasive paragraphs, chapters, and essays on any given topic. Additionally, it will exacerbate plagiarism problems in many fields, such as education, medicine, and law, and could be used to cheat on academic exams. To address this issue, some solutions are proposed, such as adopting definitional recognition techniques to detect plagiarism, and using new datasets. At the same time, a solution is proposed to guide ChatGPT to generate some critical thinking questions by asking questions, answering and critically evaluating them to avoid cheating in academic exams. This analysis also shows that ChatGPT has critical thinking and highly realistic text generation capabilities , including accuracy, relevance, depth, breadth, logic, persuasiveness, and originality. Therefore, educators must be aware that ChatGPT may be used for exam cheating and take steps to combat cheating and ensure fairness in online exams.
ChatGPT user feedback
In the study of ChatGPT user feedback by Haque et al., they extracted Twitter data and built the ChatGPTTweet dataset, which contains 18k tweets, each tweet contains text content, user location, occupation, certification status, release date and label and other information. By studying this dataset, the author answers three questions:
Characteristics of early ChatGPT users;
ChatGPT related Twitter discussion threads;
User sentiment towards ChatGPT
The study found that early ChatGPT users had diverse occupational backgrounds and geographical locations, and 9 topics related to ChatGPT were involved. Most users expressed positive emotions on topics such as software development and creativity, and only a few users expressed positive emotions on topics such as software development and creativity . concerns about the potential misuse of ChatGPT.
Adverse effects of ChatGPT on users
Regarding the negative impact of ChatGPT on users, Luan et al. studied the psychological principles of ChatGPT, deeply studied the factors that attract users' attention, and revealed the influence of these factors on future learning. After the epidemic, both teachers and students are facing uncertainty and work pressure in the teaching process. Under these common constraints of education and employment, educators and students must re-evaluate current educational methods and outcomes, as well as students' future career development. Through ChatGPT's question-and-answer exchange, people can easily obtain appropriate solutions or key information , thereby enhancing learning motivation, eliminating learning anxiety, increasing learning interest, and obtaining psychological satisfaction. Subhash et al. explore whether large language models have the ability to invert user preferences. With the development of pretrained large language models, there has been growing concern about the ability of these models to influence, persuade, and potentially manipulate user preferences in extreme cases. Therefore, there is also a rough qualitative analysis of the literature that adversarial actions do lead to potential changes in user preferences and behaviors in dialogue systems. Further quantitative analysis of the capabilities of large language models in this regard will require the use of additional statistical summarization techniques in future research.
limit
Outdated knowledge : Current models are trained on historical data (as of 2021), thus lacking real-time understanding of current events. This is a critical issue in today's era of information explosion, as the progressively less reliable prior knowledge bases may yield inaccurate responses, especially in rapidly evolving fields such as jurisprudence and technology. In addition, these models cannot be fact-checked, and the training data is composed of content from various sources, some of which may be unreliable, which may lead to plausible but meaningless responses.
Insufficient understanding : While these models can explain most queries and contextual situations, they occasionally suffer from understanding biases when dealing with ambiguous or contextually complex queries. Furthermore, in some specialized domains, the abundance of unique abbreviations exacerbates model understanding challenges, leading to incorrect and empty responses.
Energy consumption : These large-scale models require a lot of computing resources and electricity during the training and inference stages, resulting in a significant increase in energy consumption and carbon emissions. Therefore, this limits their deployment and practical applications.
Malicious use : While OpenAI has implemented a series of restrictions to mitigate the harmfulness of the models, there have been cases where users circumvent these restrictions through carefully crafted prompts, which has resulted in models producing inappropriate content or even using them for illegal commercial purposes.
Bias and Discrimination : Due to the influence of pre-trained data, large language models are biased in political, ideological and other domains. Therefore, the application of large language models in public domains, such as education and publicity, should be done with extreme caution.
Privacy and data security : As the number of users increases, protecting user privacy and data security becomes more and more important. In fact, ChatGPT was banned in Italy in early April due to privacy concerns. This is especially critical because models extensively collect personal information and preferences during interactions, and future multimodal models, such as GPT-4, may often require users to upload private photos.
future direction
Future research developments should focus on addressing the limitations of current ChatGPT and GPT-4 to improve their practical applications.
Researchers should continue to refine model training methods while filtering pre-training data to minimize the presence of misleading information in the model knowledge base to obtain accurate answers. In addition, emphasis is placed on training methods that economize computing resources to reduce costs and expand potential application scenarios.
Advances in context-aware and disambiguation techniques are expected to help improve models’ ability to understand complex queries , thereby improving the accuracy, relevance, and context-awareness of AI-generated content. Integrating real-time data streams can also keep these models in sync with current events and trends, allowing them to provide up-to-the-minute information such as real-time traffic, weather and stock updates.
Developers should engage in interdisciplinary collaboration with experts from different fields, including policy development, law, and sociology, with the aim of developing standards and ethical frameworks for LLM development, deployment, and utilization, thereby mitigating potentially harmful consequences. In terms of public awareness and education, necessary awareness training should be implemented prior to large-scale public deployment and application to increase public understanding of LLM capabilities and limitations while promoting responsible and informed use, especially in K-12 education and journalism.
The impact of ChatGPT and GPT-4 should not be limited to the field of natural language processing. They also show promise in the fields of computer vision, biomimetic artificial intelligence, and robotics. These models demonstrate learning and understanding capabilities comparable to human intelligence levels, positioning them as key components in the development of artificial general intelligence (AGI). Their ability to enable seamless interactions between humans and robots paves the way for more complex tasks to be performed. The zero-shot contextual learning capabilities of these models enable rapid adaptation to new tasks without the need for labeled data for fine-tuning, an important challenge in fields such as medical informatics and robotics, where the availability of labeled data is often limited or non-existent.
summary
This review provides a comprehensive introduction to ChatGPT and GPT-4, highlighting their potential applications and important contributions in the field of natural language processing. The findings indicate that research interest in these models is growing rapidly, and that they have shown great potential for application in various fields. One of the key factors for the success of these models is their ability to perform large-scale pre-training , acquiring massive amounts of knowledge from the Internet, enabling the models to learn from large amounts of data. Introducing reinforcement learning with human feedback further enhances the adaptability and performance of the model , making it efficient and fast in processing natural language. Several potential ethical issues related to the development and use of ChatGPT and GPT-4 were also identified. For example, there are concerns about the generation of biased or harmful content, invasion of privacy, and misuse of the technology, and it is critical to address these concerns and ensure responsible and ethical development and use of ChatGPT and GPT-4. Furthermore, the results of this study show that both ChatGPT and GPT-4 have great potential in fields such as education, history, mathematics, physics, etc., where these models can facilitate tasks such as generating summaries, answering questions, and providing users with personalized recommendations . Altogether, this review provides a useful guide for researchers and practitioners to advance the field of natural language processing. The emergence of ChatGPT and GPT-4 has injected new vitality and hope into the field of natural language processing. Future research in this field should focus on addressing ethical issues, exploring new application scenarios, and ensuring their responsible and ethical use . These models have great potential to disrupt natural language processing, and we look forward to seeing further developments.
The author of the cute house: IQ dropped all over the place
I am studying for a master's degree in computer science at BIT. I am addicted to chatting with ChatGPT recently. I am curious about all novel NLP applications. I am trying to become a slash youth with a wide range of interests~
Recommended works
Reply keywords in the background [ join the group ]
Join the NLP, CV, search promotion and job hunting discussion group