ChatGPT and Open-AI Models: A Preliminary Review
2. ChatGPT Training Process
2.1. The Architecture of the Model
2.2. Pre-Processing of Text Data
- Tokenization is a fundamental step in natural language processing that involves segmenting text into discrete units of meaning, known as tokens . The purpose of tokenization is to facilitate the subsequent processing of text by the model. In the case of ChatGPT, tokenization is performed using a pre-trained tokenizer designed explicitly for natural language processing tasks. This tokenizer converts the input text into a sequence of tokens, where each token represents a specific word or subword unit. The resulting token sequence is then used as input for the model in further processing.
- Subword encoding is a widely used technique in natural language processing to handle rare or out-of-vocabulary words in the input text. It involves breaking down the input text into smaller units or subwords, which the model can then process. Subword encoding has been shown to improve the performance of language models on various natural language processing tasks. In the case of ChatGPT, subword encoding is performed using a pre-trained subword encoder, such as the Byte Pair Encoding (BPE) algorithm, specifically designed for natural language processing tasks [27,28].
- Data cleaning is a crucial step in pre-processing text data as it aims to eliminate irrelevant or noisy information from the input text, ultimately improving the quality and suitability of the input data for the model . It involves a series of steps, such as removing punctuation, numbers, and special characters and correcting spelling and grammatical errors, among others. Data cleaning transforms the input text into a more coherent and standardized form, thereby enhancing the model’s ability to capture meaningful patterns in the data.
2.3. Training Algorithm
2.3.1. Pre-Training Phase
2.3.2. Fine-Tuning Phase
3. Literature Review
3.1. Healthcare Topics
3.2. Education Topics
3.3. General Topics
3.4. Finance Topics
3.5. Machine Learning/AI Topics
3.6. Translation Topics
3.7. Mathematical Topics
3.8. Social Topics
3.9. Industry Topics
3.10. Art Topics
3.11. Marketing Topics
- Research: Observations gleaned from the utilization of ChatGPT suggest that the platform can identify and proffer academic references that pertain to specific sentences or paragraphs, thereby facilitating the work of prospective researchers in their search for relevant sources. However, it was noted that most of the proposed references, while related to the topic, must be more present in academic databases and often feature errors in author names or digital object identifiers (DOIs). Following the identification of such errors, ChatGPT endeavored to rectify the situation by suggesting alternative sources, but these, too, were found to be erroneous upon scrutiny. In response to these repeated instances of inaccuracy, ChatGPT expressed regret at its inability to offer suitable alternatives. The genesis of this particular issue could be attributed to an error that occurred during the training phase of the model, an anomaly that the developers have yet to uncover. Ultimately, it is imperative to emphasize that researchers should exercise extreme caution and refrain from relying solely on tools such as ChatGPT to procure references to pertinent scholarly articles.
- Programming: In an attempt to confirm the rumors about the programming proficiencies of ChatGPT, the researchers provided the platform with a prompt describing the desired program features. The output in the Python programming language was remarkable. However, it was observed that composing an appropriate prompt effectively conveying the desired request to ChatGPT necessitates a certain degree of programming knowledge. Furthermore, while the output was impressive, comprehending and adapting it to meet the specific demands of a project also requires programming expertise. To summarize, ChatGPT can be a valuable tool in aiding developers in the generation of code, but it cannot supplant the developer’s role, at least not at present.
- To what extent will the advancement of science be expedited and streamlined by empowering scientists with instantaneous access to information by posing relevant queries to ChatGPT?
- The primary objective of this research is to generate knowledge for future applications. However, the process of a new scientist gathering information from existing literature and using it to expand the field can lead to significant delays in time. While this time is not necessarily wasted, it may impede the discovery of fresh ideas that could more expeditiously solve pressing human problems. AI tools, such as ChatGPT, could enable new scientists to read the fundamental concepts of a field and pose pertinent questions within a few hours. Nevertheless, AI tools must evolve to offer knowledge commensurate with the level of expertise expected by scientists. In this vein, neophyte scientists could commence the development of the technology from the outset, drawing upon prior research in the field. A compelling example of this pertains to the potential acceleration and efficiency with which novel medical treatments can be devised for emerging diseases.
- Does the continued existence of search engines, as we have known them thus far, remain justified?
- To date, the conventional approach to utilizing search engines has involved posing search queries and awaiting the corresponding search results. Subsequently, individuals browse through a plethora of websites suggested by search engines in hopes of obtaining the requisite knowledge. Yet, how often have we altered the search term (i.e., keyword) due to unrepresentative outcomes? The advent of AI tools such as ChatGPT will transform the conventional search methodology. By conversing with the AI tool and enabling it to filter information on our behalf, the most suitable outcomes will be instantly accessible without any time wastage (Microsoft Bing: Reinventing search with a new AI-powered). Nonetheless, it is critical to underscore that the searcher’s capacity for critical thinking is likely to be significantly reduced.
- Does the emergence of ChatGPT and other AI tools signal the demise of eCommerce as we know it today?
- The integration of ChatGPT and other AI tools into eCommerce platforms may disrupt the traditional way of online shopping. Previously, consumers reached online stores through search engines or third-party platforms, selecting products that align with their needs using built-in eCommerce filters. However, with the integration of ChatGPT in searches, specific products, and online stores may be suggested, which may not necessarily be the most cost-effective or highest-quality options. There is a potential threat of specific recommended products being prioritized to bring more significant benefits to the company that created the AI tool. As such, the introduction of AI tools in eCommerce platforms raises concerns about the potential impact on consumer choice and the overall integrity of the online shopping experience.
- To what extent will individuals utilize ChatGPT or comparable AI tools for disease diagnosis and treatment?
Data Availability Statement
Conflicts of Interest
|ChatGPT can generate human-like responses, improving the quality of chatbot interactions .||Biases in the training data: Language models similar to ChatGPT rely heavily on the data they are trained on, and if that data is biased in some way (e.g., if it contains more examples of certain types of language or perspectives), the model may reproduce those biases in its responses .|
|ChatGPT can understand and respond to complex and nuanced user inputs, allowing for more natural and effective communication .||Lack of understanding of context: Although ChatGPT can generate responses based on the preceding conversation, it does not have a deep understanding of context or the larger discourse in which a conversation takes place. This can lead to responses that are inappropriate or nonsensical .|
|ChatGPT can be trained on large datasets, improving its ability to generate high-quality responses .||Difficulty with sarcasm and irony: Because ChatGPT does not have a full understanding of human emotions and intentions, it may have difficulty recognizing sarcasm or irony and may respond inappropriately as a result .|
|ChatGPT can be fine-tuned on specific domains, allowing for more specialized and accurate responses .||Limited knowledge of the physical world: ChatGPT does not have direct access to the physical world and, therefore, may not be able to answer questions that require knowledge of the physical environment (e.g., “What’s the weather like outside?”) .|
|ChatGPT can handle multiple languages, improving accessibility for users across different countries and regions .||Difficulty with long-term memory: While ChatGPT is able to generate responses based on previous statements in a conversation, it may not be able to remember specific details from earlier in the conversation or from previous conversations .|
|ChatGPT can generate coherent and contextually appropriate responses, improving the overall chatbot user experience .||Inability to generate truly creative responses: While ChatGPT is capable of generating novel responses, it may struggle to produce truly creative responses that go beyond what it has seen in the training data .|
|ChatGPT can learn from user feedback and improve over time, leading to better performance and user satisfaction .||Difficulty with multi-party conversations: ChatGPT is primarily designed to handle two-party conversations and may struggle to keep track of multiple speakers or to distinguish between different speakers in a multi-party conversation .|
|ChatGPT can be used for a variety of applications beyond chatbots, such as natural language processing and text generation .||Difficulty with non-standard English: ChatGPT has been trained primarily on standard English text and may have difficulty with non-standard varieties of English, such as regional dialects or slang .|
|ChatGPT can be used for tasks such as summarization, translation, and question-answering, improving efficiency and productivity in various fields .||Difficulty with complex sentence structures: ChatGPT may struggle with complex sentence structures, particularly those involving nested clauses or unusual grammatical constructions .|
|ChatGPT can be used for research purposes, allowing for new insights and discoveries in the field of natural language processing .||Limited ability to reason abstractly: While ChatGPT is able to generate responses based on patterns in the training data, it may not be able to reason abstractly or understand complex logical relationships .|
|ChatGPT can be fine-tuned on smaller datasets, allowing for more accessible and cost-effective applications .||Inability to recognize or understand images: While ChatGPT is capable of processing text, it does not have the ability to recognize or understand images, which can limit its usefulness in certain applications .|
|ChatGPT can be used for educational purposes, such as teaching language skills and improving language proficiency .||Limited ability to generate coherent narratives: While ChatGPT is able to generate text that follows logically from previous statements in a conversation, it may struggle to generate coherent narratives that have a clear beginning, middle, and end .|
|ChatGPT can be used for creative applications, such as generating poetry or other forms of creative writing .||Inability to distinguish between fact and fiction: ChatGPT is not able to distinguish between statements that are factually true and those that are fictional or speculative, which can lead to inappropriate or inaccurate responses .|
|ChatGPT can be used for data augmentation, improving the performance of machine learning models in various applications .||Difficulty with social and cultural nuances: ChatGPT may not have a full understanding of social and cultural nuances, such as sarcasm or humor, and may respond inappropriately as a result .|
|ChatGPT can be used for generating chatbot training data, reducing the need for human annotation, and improving efficiency .||Lack of human-like empathy: ChatGPT does not have the ability to understand human emotions in the same way that humans do, which can limit its ability to provide appropriate emotional support or responses .|
|ChatGPT can be used for generating natural language queries for databases, improving accessibility and ease of use .||Limited ability to handle complex tasks: While ChatGPT can perform a range of tasks, its ability to handle complex tasks, such as problem-solving or decision-making, may be limited .|
|ChatGPT can be used to improve search engine results by generating more natural and accurate search queries .||Dependence on large amounts of data: ChatGPT relies heavily on large amounts of training data, which can be difficult and expensive to obtain and may lead to challenges in terms of scalability and generalizability .|
|ChatGPT can be used for generating product descriptions or reviews and improving the efficiency and quality of e-commerce websites .||Lack of transparency: The inner workings of ChatGPT are opaque to most users, which can make it difficult to understand why it generates certain responses and can limit trust in the system .|
|ChatGPT can be used to improve social media marketing by generating engaging and natural language responses to customer inquiries .||Vulnerability to adversarial attacks: Language models similar to ChatGPT are vulnerable to adversarial attacks, in which an attacker deliberately manipulates input data in order to produce unexpected or malicious output .|
|ChatGPT can be used to improve customer service in various industries by providing more effective and personalized responses to customer inquiries .||Environmental impact: Training large language models similar to ChatGPT requires significant amounts of computational power and energy, which can have a significant environmental impact .|
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|Research Topic||Strong Statements||Capabilities Examined||Citation Indicator|
|Healthcare/Exams||ChatGPT can produce understandable reasoning and provide relevant clinical insights, increasing confidence in its trustworthiness and comprehensibility .||Reasoning/Logical Reasoning||140|
|Healthcare||ChatGPT does not produce original texts after paraphrasing .||Research/Paraphrasing/Academic Writing||63|
|Healthcare/Vaccine Effectiveness||ChatGPT could be an excellent research tool for conducting analyses and drafting research articles, but it could not be trusted to find article references .||Research/Academic Writing||18|
|Healthcare/Healthcare Education and Research||The proactive embrace of LLM technologies, with careful consideration of ethical and legal issues, can expedite innovation in healthcare .||-||11|
|Healthcare||ChatGPT lacks context, inaccuracy, bias, and understanding of the nuances of medical sciences and language .||-||8|
|Healthcare/Education||ChatGPT commits errors in basic reasoning, logic, mathematics, and presenting factual information .||Research/Reasoning/Calculations||8|
|Healthcare/Biomedical||Even though ChatGPT’s responses were methodical, accurate, and innovative, they lacked the quality expected of scholarly writing .||Research/Reasoning/Academic Writing||8|
|Healthcare/Dentistry||ChatGPT can be used to oversee the telemonitoring of patients, furnish virtual training settings, and enhance evaluations of students and the care of patients .||Reasoning/Critical Thinking||7|
|Healthcare/Diagnosis||ChatGPT provided correct diagnoses with more than 90% accuracy .||Research||4|
|Healthcare/Orthodontics/Diagnosis||ChatGPT could monitor more patients simultaneously compared to traditional treatment management methods ||Research||3|
|Healthcare/Pharmacy||ChatGPT could provide only general knowledge regarding the chosen topic and is incapable of providing a comprehensive analysis .||Research/Reasoning/Academic Writing||2|
|Healthcare/Diagnosis||ChatGPT achieved an impressive level of accuracy in clinical decision-making, but it might face difficulties in making a diagnosis based on a canonical presentation .||Research/Understanding/ Reasoning||0|
|Healthcare/Drug Discovery||ChatGPT’s model’s performance is directly linked to the data quality used for its training .||Reasoning/Research||0|
|Healthcare/Orthodontic||ChatGPT can be used to enhance patient care and outcomes .||-||0|
|Healthcare/Pediatric||While there are challenges associated with the use of ChatGPT in pediatric research, there are also opportunities for language models to make significant contributions to the field .||-||0|
|Education/Teaching||ChatGPT could assist teachers by automating various tasks such as assessment, plagiarism detection, administration, and feedback mechanisms .||-||58|
|Education/Exams||Responses given by ChatGPT were on-topic and relevant, achieving high scores on precision, relevance, depth, and originality .||Research/Critical Thinking||52|
|Education/Chatbots||ChatGPT is extremely important and valuable for transforming education .||-||19|
|Education/Teaching||It is crucial to provide resources and training for educators to use ChatGPT effectively .||Teaching Assistance||0|
|Education/Teaching||Educators should guide students on effective questioning techniques and the validation of responses while developers improve the accuracy of ChatGPT .||Teaching Assistance/Reasoning||0|
|Education/Academic Writing||Should be created ethical guidelines to ensure the responsible use of AI language models in scientific publishing .||Research/Academic Writing||0|
|General||ChatGPT demonstrates greater proficiency in deductive and abductive reasoning as opposed to inductive reasoning .||Reasoning/Logical Reasoning||49|
|General||ChatGPT’s ability to fix bugs significantly surpasses the outcomes achieved by conventional program repair approaches .||Code Debugging||21|
|General||Current benchmarks on ChatGPT cannot adequately address a significant proportion of ethical concerns .||Ethics||27|
|General||ChatGPT outperformed the RoBERTa-large model on various tasks. ChatGPT achieves a similar level of comprehension as some BERT-style models. Falls short of outperforming the current top models on specific natural language understanding tasks .||Linguistics/Paraphrasing/Understanding||12|
|Finance/Cryptocurrency||ChatGPT’s public data outperform private data .||Research/Academic Writing||44|
|Finance||ChatGPT’s responses were compared against students’ answers outperforming the average college student .||Understanding/Reasoning||5|
|Machine Learning/AI/Natural Language Processing||ChatGPT performs superior to GPT-3.5, highlighting its remarkable arithmetic reasoning ability .||Reasoning/Calculations||32|
|Machine Learning/AI||The text generated by ChatGPT has less coherence and feelings than the text generated by a human .||Research||5|
|Machine Learning/Data Science||ChatGPT enhances the productivity and accuracy of data science workflows .||-||5|
|Machine Learning/AI/Chatbots||From the perspective of the development objectives of conversational chatbots, the main objective was not only to improve technical aspects by providing accurate responses but to ensure that users’ needs are met through context maintenance .||Dialogue System/Logical Reasoning||0|
|Machine Learning/Frameworks||There is a great need for a framework to bridge the gap between artificial and natural systems .||-||0|
|Translation||ChatGPT competes favorably with commercially available translation products. Its performance lags significantly behind on low-resource or foreign languages .||Translation||40|
|Mathematics||ChatGPT’s mathematical skills are noticeably worse than those of a typical mathematics graduate student .||Calculations||24|
|Social/Early Reactions||Concerns arose about the next evolution of jobs, the new technological landscape, the quest for artificial general intelligence, and the ethics-progress conundrum .||-||17|
|Social/Megatrends||The AI model did not entirely comply with the word count specifications. There was a minor software error in the GPT-3 generated code during the debugging process .||Research/Classification/Academic Writing||7|
|Social/Politics||ChatGPT’s responses are left-leaning political viewpoints .||Research/Ethics||1|
|Social/Social Impact||Although ChatGPT’s usefulness, there are potential risks associated with its use .||-||1|
|Social/Security||ChatGPT can be used for social engineering attacks .||Security||0|
|Industry/Robotics||Involving a human in the loop is essential to oversee and step in if ChatGPT produces unexpected behaviors .||Application||6|
|Industry/Intelligent Vehicles||ChatGPT lacks updated data .||Understanding/Reasoning||4|
|Industry/Intelligent Vehicles||There are potential conflicts between legal requirements and user intentions that could affect ChatGPT’s integration into smart vehicles .||-||2|
|Industry/Construction||ChatGPT can be used to generate a construction schedule for a simple construction project .||Application||2|
|Industry/Automation||While ChatGPT’s understanding of Industries 5.0 is basic and shallow .||Understanding/Research||1|
|Industry/Supply Chain||ChatGPT may support, but not replace, the ability of supply chain experts in decision-making .||-||0|
|Art||ChatGPT can improve the creativity and productivity of parallel art .||Understanding/Art||2|
|Marketing||ChatGPT has the potential to revolutionize marketing if ethical considerations are taken into account .||-||0|
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Roumeliotis, K.I.; Tselikas, N.D. ChatGPT and Open-AI Models: A Preliminary Review. Future Internet 2023, 15, 192. https://doi.org/10.3390/fi15060192
Roumeliotis KI, Tselikas ND. ChatGPT and Open-AI Models: A Preliminary Review. Future Internet. 2023; 15(6):192. https://doi.org/10.3390/fi15060192Chicago/Turabian Style
Roumeliotis, Konstantinos I., and Nikolaos D. Tselikas. 2023. "ChatGPT and Open-AI Models: A Preliminary Review" Future Internet 15, no. 6: 192. https://doi.org/10.3390/fi15060192