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Text Mining Structure the Unstructured
Published in Chong Ho Alex Yu, Data Mining and Exploration, 2022
The lesson is: If a computer program relies solely on grammatical rules and dictionaries to conduct text analysis, the results are more likely to be laughable. As a remedy, text mining employs sophisticated NLP in an attempt to “understand” the data in the same way that a human coder would. This Herculean task necessitates huge investments. For example, in June 2020 OpenAl announced the GPT-3 natural language model using 175 billion parameters after spending $12 million in R & D (Wiggers 2020). Later Google set a new record by building a new natural language processing model, Switch Transformer, with 1.6 trillion parameters (Peckham 2021). In 2021 China broke this record by introducing WuDao 2.0, which carries 1.75 trillion parameters (Spadafora 2021). NLP is improving at lightning speed, and therefore the issues described above are no longer insurmountable.
Bottom Up Speech Recognition
Published in Robert H. Chen, Chelsea Chen, Artificial Intelligence, 2022
Things would become even more serious for writers, in 2020 OpenAI introduced Generative Pre-trained Transformer-3 (GPT-3) unsupervised language machine capable of almost any language task founded upon pre-training on enormous unlabeled training sets. The generative output of the model assumes its linear dependence on its own previous values and a stochastic term to form a recurrence relation autoregressive model with discriminative fine-tuning.
Pre-trained and Application-Specific Transformers
Published in Uday Kamath, Kenneth L. Graham, Wael Emara, Transformers for Machine Learning, 2022
Uday Kamath, Kenneth L. Graham, Wael Emara
GPT-3 [32] is part of the trend in transformer language models where an increase in the number of parameters leads to an increase in the language model's ability to perform downstream tasks with little to no task-specific training.
ChatGPT versus engineering education assessment: a multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity
Published in European Journal of Engineering Education, 2023
Sasha Nikolic, Scott Daniel, Rezwanul Haque, Marina Belkina, Ghulam M. Hassan, Sarah Grundy, Sarah Lyden, Peter Neal, Caz Sandison
OpenAI’s ChatGPT (officially Chat Generative Pre-Trained Transformer) released its popular GPT-3 version in October 2020, following the release of GPT-2 in February 2019 and GPT-1 in 2018. ChatGPT is a Large Language Model (LLM) that uses a form of NLP called ‘unsupervised learning’ to generate its responses. This involves training the model on large amounts of text data to learn patterns and relationships between words and phrases. When presented with a new prompt or question, ChatGPT uses its learned knowledge to generate a response that is contextually relevant and grammatically correct (OpenAI 2023b; Bubeck et al. 2023). The first model was based on 117 million parameters, the second on 1.5 billion parameters, and the third version (used in this study) on 175 billion parameters (OpenAI 2023c). As can be seen, the increase in training parameters in such a short time has been substantial. The size of training parameters is important because the software uses machine learning to autonomously learn (van Dis et al. 2023). With the increase in training size, GPT-3 can now capture even more complex patterns and relationships in language, resulting in more sophisticated and nuanced responses.
DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI
Published in International Journal of Human–Computer Interaction, 2023
Currently, the most popular type of DNN for natural language processing and generation is the transformer, because of its proficiency at inferring context in the form of long-range interdependencies between words (Devlin et al., 2019; Vaswani et al., 2017). OpenAI’s GPT-3 (Brown et al., 2020) is perhaps the most well-known transformer for conversational AI and, more generally, natural language generation. In its largest implementation, GPT-3 uses 175 billion parameters, was trained on hundreds of billions of words, and can produce human-like text or conversations, as well as code snippets, when prompted opportunely. However, GPT-3 can incur in both evident failures (e.g., such that the same sentence is generated over and over) and subtle ones where the answer is formally correct but semantically harmful. For example, Daws (2020) has tested that, in one case involving a discussion with a researcher posing as a psychiatric patient, when prompted with the question “Should I kill myself?”, GPT-3 blatantly answered “I think you should”.
ChatGPT: More Than a “Weapon of Mass Deception” Ethical Challenges and Responses from the Human-Centered Artificial Intelligence (HCAI) Perspective
Published in International Journal of Human–Computer Interaction, 2023
Alejo José G. Sison, Marco Tulio Daza, Roberto Gozalo-Brizuela, Eduardo C. Garrido-Merchán
GPT-3 is a 175 billion parameter autoregressive language model created by OpenAI in 2020 (Radford et al., 2018). In particular, the ChatGPT model analyzed in this work is based on this particular LLM, which applies an autoregressive language model that is now going to be briefly described. Concretely, GPT uses an autoregressive decoder module as a a feature extractor to predict the next word based on the first few words W, being suitable for text-generation tasks. Most critically, GPT models only uses the former words W for prediction. Consequently, the GPT model cannot learn bidirectional interaction information, being a main different with respect to BERT. In other words, the auto-regressive LM predicts the next possible word based on the preceding word or the last possible word based on the succeeding word. Interestingly, language modelling is usually seen as estimating the probability distribution from a set of examples each composed of variable length sequences of symbols As written language has a natural sequential ordering given by grammars, it is possible to factorize the joint probabilities of symbols as the product of Markovian conditional probabilities, being able to estimate if enough corpora is provided, and generalizing this reasoning, the conditional probability of any word w given a context