What type of learning model helps chatbots improve response accuracy over time?

Machine learning models are designed to improve their performance as they are exposed to more data over time. In the context of chatbots, these models utilize algorithms that allow the system to learn from past interactions, recognize patterns, and adjust responses accordingly. This iterative learning process enables chatbots to enhance their response accuracy and relevance based on user interactions and feedback.

Unlike static learning models, which do not evolve or change once they are set up, machine learning models are dynamic and can adapt to new information. Repetitive learning models and manual programming models do not provide the same level of flexibility and adaptability, as they either rely on pre-defined rules or do not use data-driven approaches to refine performance.

Thus, the ability of machine learning models to continuously learn and improve from data makes them the most effective choice for increasing the accuracy of responses in chatbots over time.