How ChatGPT and Other LLMs Work—and Where They Could Go ...
AI-powered chatbots are taking over the world by providing a wide range of services, from web searches to generating creative literature and managing data. ChatGPT and Google Bard are examples of large language models (LLMs) that are leading the conversational AI industry. To better understand these bots and their capabilities, let's take a deep dive into how they work and where they could go.
Leveraging the Power of Data
Like many AI systems, LLMs rely on vast amounts of data to work. However, the source of this data is not always clear. The research paper introducing the LaMDA model, which Bard uses, reveals its data sources include Wikipedia, public forums, and code documents from sites related to programming. Other sites like Reddit and StackOverflow are also likely to be used as sources for training LLMs.
The Role of Neural Networks
LLMs use a combination of machine learning and human input to process and interpret text data. Specifically, they use neural networks, which are made up of multiple nodes and layers, to process language. LLMs use a specific architecture of neural networks called transformers, which can read vast amounts of text, identify patterns in language, and make predictions about what words should come next. The self-attention mechanism is a key feature of the transformer architecture, which helps to contextualize words in a sentence and improve comprehension.
Randomness and Variations
Although LLMs are powerful tools, their responses are not always predictable. The code is designed to have some randomness and variation, which means the same question might elicit different responses. Moreover, LLMs do not always choose the most likely word to follow another. Rather, they generate a response based on the data they have been trained on. As a result, their responses can sometimes be generic or clichéd.
Humans in the Loop
Humans play a role in training LLMs by pointing out mistakes and ranking answers based on how good they are. This process is known as “reinforcement learning on human feedback” (RLHF). LLMs use this feedback to refine their internal neural networks, which improves their responses over time. As LLMs get more complex, their capabilities will continue to improve, making them even more useful for a wider range of applications.
The Future of LLMs
As LLMs become more advanced, they will be able to perform a wider range of tasks. For instance, they could be used to facilitate communication between humans and animals, or between humans and machines. They could also help researchers better understand how humans communicate and learn. Although LLMs still have some limitations, they have the potential to revolutionize the world we live in.
In conclusion, LLMs such as ChatGPT and Google Bard are powerful conversational AI tools that rely on vast amounts of data and neural networks to process language. Although their responses can be unpredictable, they have the potential to revolutionize numerous industries.
