How ChatGPT Makes Decisions: A Simple Explanation
ChatGPT and other AI-driven chatbots are capable of producing fluent and grammatically correct sentences that may impressively mimic the rhythm of natural human speech. However, it is essential to understand that this well-executed dialogue does not indicate any thought, emotion, or intention on the part of the chatbot.
The functioning of a chatbot is akin to a machine that performs mathematical calculations and statistical analysis to generate appropriate words and sentences in response to specific contexts. The process involves extensive training and feedback from human annotators to simulate real conversations effectively.
To interact with human users, chatbots like ChatGPT are trained using vast amounts of conversational data that teach the machines how to engage with people. OpenAI, the company behind ChatGPT, states that its models rely on information from diverse sources, including user input and licensed materials.
These AI chatbots, including OpenAI’s ChatGPT, are based on large language models (LLMs). These models are trained on extensive volumes of text obtained from published writings and online information created by humans. The training enables the models to understand the significance of words and patterns of speech, enhancing their ability to provide appropriate responses.
Moreover, chatbots undergo further training by humans to learn how to deliver suitable responses and avoid generating harmful messages. They can be instructed to recognize and avoid toxic or political content and frame responses accordingly.
When a chatbot is tasked with answering a straightforward factual question, the process is relatively simple. The bot employs algorithms to select the most probable sentence for its response. It quickly presents one of the top choices, selected at random. As a result, asking the same question repeatedly may yield slightly different answers.
In more complex scenarios, the chatbot can break down questions into multiple parts and answer them sequentially, utilizing its previous responses to generate subsequent ones. For example, if asked to name a US president who shares a first name with the male lead actor of the movie Camelot, the bot may first provide the actor’s name (Richard Harris) and subsequently use that information to answer the original question (Richard Nixon).
However, when confronted with a question to which it does not possess the answer, chatbots face a significant challenge—their inherent inability to know what they don’t know. Consequently, they may make an educated guess based on existing knowledge and present it as a factual response. This phenomenon is known as hallucination, where the chatbot invents information.
According to William Wang, an associate professor at the University of California, Santa Barbara, this lack of understanding regarding the unknown is a limitation in the chatbot’s metacognition or knowledge of knowledge.
It is crucial to comprehend the inner workings of chatbots like ChatGPT. They rely on extensive training, learn from human feedback, and analyze patterns in language to provide appropriate responses. However, they have limitations and may sometimes present speculative information as factual. Understanding these aspects helps users navigate and interpret the responses they receive.
