Generative pre-trained transformer

Download a PDF of the paper titled BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining, by Renqian Luo and 6 other authors Abstract: Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e., BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. We evaluate BioGPT on six biomedical NLP tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms. Code is available at

Natural language computer applications are becoming increasingly sophisticated and, with the recent release of Generative Pre-trained Transformer 3, they could be deployed in healthcare-related contexts that have historically comprised human-to-human interaction. However, for GPT-3 and similar applications to be considered for use in health-related contexts, possibilities and pitfalls need thoughtful exploration. In this article, we briefly introduce some opportunities and cautions that would accompany advanced Natural Language Processing applications deployed in eHealth. Natural Language Processing (NLP) has a long history in clinical informatics and includes groundbreaking work using computer-based algorithms that compute on text and natural language. There are many clinical applications of NLP including assisting with provider documentation, automated structured chart abstraction, and in machine learning GPT-3 is an autoregressive language model trained with 175 billion parameters and then tested in “few-shot learning settings” (in which a new language task can be performed after only a few examples). Autoregressive language models predict the next element in a text, usually a word, based on previous natural language texts. Although its developers at OpenAI think it performs well on translation, question answering, and cloze tasks (e.g., a fill-in-the-blank test to demonstrate comprehension of text by providing the missing words in a sentence) It is within this caveat-f...

Generative Pre-trained Transformer (GPT) is a family of large-scale language models developed by OpenAI. GPT models are based on a transformer architecture that has been pre-trained on vast amounts of text data using unsupervised learning. The pre-training process involves training the model to predict missing words or next words in a sentence, and then fine-tuning the model on a specific downstream task such as language translation, text classification, or question answering. GPT-3, the latest and largest version of the GPT model, has been trained on a massive corpus of text data that includes books, articles, and websites, and contains 175 billion parameters, making it one of the largest language models ever created. GPT-3 can generate human-like text, complete sentences, paragraphs, and even entire articles, and can perform a wide range of NLP tasks with remarkable accuracy. The success of GPT models has sparked significant interest in the field of natural language processing, and has led to the development of many other large-scale language models that are now being used in a wide range of applications, from chatbots and virtual assistants to text analysis and summarization tools. Hi, I am Steve, a digital business consultant offering services such as SEO, Google Ads management, software & web development, social media automation, and conversion rate optimization. I focus on brand, strategy, AI, innovation, operations, and security to satisfy your needs as a digital bu...

Download a PDF of the paper titled GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models, by Tyna Eloundou and 3 other authors Abstract: We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect o...

The arrival in late 2022 of the ChatGPT chatbot represented a milestone in artificial intelligence that took decades to reach. Scientists were experimenting with “computer vision” and giving machines the ability to “read” as far back as the 1960s. Today it’s possible to imagine a computer performing many human tasks better than people can. Whether you’re worried about being replaced by a robot, or just intrigued by the possibilities, here are some frequently used AI buzzwords and what they mean. ML is the process of gradually improving algorithms — sets of instructions to achieve a specific outcome — by exposing them to large amounts of data. By reviewing lots of “inputs” and “outputs,” a computer can “learn” without necessarily having to be trained on the specifics of the job at hand. Take the iPhone photo app. Initially, it doesn’t know what you look like. But once you start tagging yourself as the face in photos taken over many years and in a variety of environments, the machine acquires the ability to recognize it. These products can hold conversations with people on topics ranging from historical trivia to new food recipes. Early examples are the tools that service providers use on their “Contact Us” pages as a first resource for customers needing help. It’s expected that chatbots such as OpenAI’s ChatGPT and Google’s Bard will improve rapidly as a result of recent advances in AI and transform how we search the internet. This refers to the production of works — pictur...

Further information: OpenAI introduced the first GPT model (GPT-1) in 2018, publishing a paper called "Improving Language Understanding by Generative Pre-Training." Capabilities [ ] OpenAI stated that GPT-4 is "more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5." To gain further control over GPT-4, OpenAI introduced the "system message", a directive in When instructed to do so, GPT-4 can interact with external interfaces. tags to perform a web search, the result of which would be inserted into the model's prompt to allow it to form a response. This allows the model to perform tasks beyond its normal text-prediction capabilities, such as using A 2023 article in Aptitude on standardized tests [ ] GPT-4 demonstrates aptitude on several standardized tests. OpenAI claims that in their own testing the model received a score of 1410 on the Medical applications [ ] Researchers from Microsoft tested GPT-4 on medical problems and found "that GPT-4, without any specialized prompt crafting, exceeds the passing score on A report by In April 2023, Microsoft and Limitations [ ] Like its predecessors, GPT-4 has been known to GPT-4 also lacks transparency in its decision-making processes. If requested, the model is able to provide an explanation as to how and why it makes its decisions but these explanations are formed post-hoc; it's impossible to verify if those explanations truly reflect the actual process. In many cases, when asked to explain its lo...