When dealing with ambiguous or context-dependent queries, GPT relies on its ability to analyze the surrounding words and context of the query. It uses a transformer architecture that processes information in parallel, making connections between words to understand the overall context.
Additionally, GPT is pre-trained on a large dataset, allowing it to draw on a wide range of knowledge and information when generating responses. This deep learning model uses probabilities to predict the most likely word or phrase that would follow in a given context.
Furthermore, GPT continues to learn and improve over time through fine-tuning and exposure to more data, enhancing its ability to handle ambiguous queries with greater accuracy.
