ChatGPT/GPT is a language model (Large Language Model LLM) that was developed by OpenAI and can use artificial intelligence to generate texts that appear to be written by humans in terms of structure and language - e.g. in the form of answers, explanations or dialogues.
OpenAI has released several versions of GPT, starting with GPT-1 through to GPT-4, with each generation significantly increasing the model size, performance and range of applications.
Transformer architecture
GPT (Generative Pre-trained Transformer) is based on a transformer architecture for neural networks that was specially developed for processing text. It uses a so-called self-attention method, which enables the model to recognise which words in the sentence are particularly important in order to understand contexts and generate suitable text output.
After training, GPT can be used to generate texts, answer questions, summarise content and much more.
GPT (Generative Pre-trained Transformer) is based on a transformer architecture for neural networks that was specially developed for processing text. It uses a so-called self-attention method, which enables the model to recognise which words in the sentence are particularly important in order to understand contexts and generate suitable text output.
After training, GPT can be used to generate texts, answer questions, summarise content and much more.
ChatGPT: Dialogue system
ChatGPT is a specific application of the GPT models that is designed for conversational use. It uses a variant of GPT that has been additionally adapted for use in dialogue systems - e.g. through fine-tuning, feedback loops (reinforcement learning from human feedback, RLHF) and special interfaces. It can draw on context from previous interactions in order to conduct coherent dialogues - depending on the technical implementation and session configuration.
ChatGPT is a specific application of the GPT models that is designed for conversational use. It uses a variant of GPT that has been additionally adapted for use in dialogue systems - e.g. through fine-tuning, feedback loops (reinforcement learning from human feedback, RLHF) and special interfaces. It can draw on context from previous interactions in order to conduct coherent dialogues - depending on the technical implementation and session configuration.
%20GPT%20vs%20ChatGPT.png?width=815&height=533&name=(Englisch)%20GPT%20vs%20ChatGPT.png)
Fast text creation and text processing
GPT and ChatGPT can be used in various areas, such as text creation, chatbots, translation and content marketing.
GPT and ChatGPT can be used in various areas, such as text creation, chatbots, translation and content marketing.
GPT models such as ChatGPT enable the fast and scalable creation of texts that can be linguistically coherent and stylistically customised. They can be used flexibly, for example for dialogue systems, text summaries or creative writing tasks. One of the advantages of such models is that they can generate a wide variety of text types automatically with little effort and at the touch of a button.
Problems: Misinformation and stereotypical representations
However, there are also clear limitations: GPT can generate misinformation or unverifiable content as it has no intrinsic knowledge of the world but is based on statistical patterns from training data. There is no reference to current or verified facts if there is no access to external data sources. There is also a risk that prejudices, stereotypical representations or distorted perspectives will be reproduced from the training data. Critical examination, post-processing and responsible use of the generated content is therefore essential.
However, there are also clear limitations: GPT can generate misinformation or unverifiable content as it has no intrinsic knowledge of the world but is based on statistical patterns from training data. There is no reference to current or verified facts if there is no access to external data sources. There is also a risk that prejudices, stereotypical representations or distorted perspectives will be reproduced from the training data. Critical examination, post-processing and responsible use of the generated content is therefore essential.