Understanding chatbots simply: This is how ChatGPT works

ChatGPT impressively demonstrates what omnipresence means in 2023: Whether in the news, social media or Internet forums, wherever you look, you hear about the revolutionary technology. While the opportunities offered by ChatGPT are largely no longer a secret, one essential point remains a gray spot for most people: How does the technology around the intelligent chatbot actually work?

In this article, we look at the most important components of the model behind ChatGPT and address the question of how the chatbot gets its information and then generates an answer. If you are taking your first steps in this topic, then you've come to the right place.

Attention: Artificial intelligence is a very profound, complex and multi-layered topic. There are entire papers and papers about how ChatGPT works. So please note that this blog article cannot go into detail about all facets behind the technology due to lack of time and is considered the first walking aid.

If you want to learn more about artificial intelligence and machine learning afterwards, we recommend that you consult further literature, textbooks, and online resources to develop a deeper understanding of the complex aspects of artificial intelligence and machine learning.

Let's start now with the essential components of AI.

1. Data collection and preparation:

The model is trained with a large amount of text data. ChatGPT used various data sources, such as the Internet, news articles and social media, to develop a broad understanding of human language.

The collected data is cleaned and prepared accordingly, i.e. irrelevant information or duplicates are sorted out and texts are divided into smaller units (so-called tokens).

The prepared data is compiled into a training corpus. This corpus is the basis for pre-training the model. The more extensive and diverse the corpus is, the better the model can develop a general understanding of language.

The model is then pre-trained on this training corpus. During this preliminary training, the model learns to recognize patterns in language, understand semantic relationships, and use contextual information.

Data collection and preparation enables the model to develop a broad understanding of human language and to process a wide range of queries in chat.

2. Model architecture

Another important component in how the model works is the architecture of ChatGPT.

This is based on the transformer architecture. This architecture makes it possible to understand and generate complex relationships in long sections of text. The model consists of many layers of attention mechanisms that run in parallel to accentuate relevant parts of the input.

Another crucial element is so-called positional encoding, which takes into account the spatial structure of text data without sequential processing. The use of encoders and decoders enables efficient processing of input information and the generation of coherent outputs.

Overall, ChatGPT's transformer architecture enables comprehensive processing of contextual information across longer sections of text, resulting in its impressive ability to respond to a wide range of user input with meaningful answers.

3. The output

The model uses the patterns, information, data, and preparation learned from training to generate a new sequence of tokens that represent the response to user input. This step is done based on the weightings and relationships learned during pre-training.

The generated token sequence is converted into human-readable text. This is the text that the chatbot outputs as an answer.

Finally, the generated response is presented to the user.

conclusion

Data collection and preparation as well as the transformer architecture are central components. Based on training, the model generates a response sequence that is presented to the user. The article provides a basic insight and recommends further literature if you are interested in more depth. ChatGPT shows that the future of artificial intelligence is promising.