Identifying a customer’s intent correctly is key to a positive customer support experience. Our project demonstrates how Large Language Models (LLMs) can revolutionize conversational AI by accurately understanding user queries. This task, known as intent detection, used to be difficult because words can have different meanings in different contexts. However, with thorough research on LLMs, we are able to tackle these challenges.
We begin by collecting numerous examples of conversations that people might have with our chatbot. Then, we utilize a special kind of language model called BERT to train our model on how to comprehend these conversations.
In the initial phase of our project, we load a JSON file containing intents designed for the chatbot, to make it suitable for data processing tasks such as text cleaning, extracting patterns, stop words removal, stemming, and organizing them into a structured format.
In the model building phase, we used the BERT model specifically for advanced language understanding capabilities. The trained model is evaluated on the test dataset, and evaluation metrics are calculated.
The prototype developed using the provided code offers a foundation for a wide range of applications. By combining language understanding capabilities with speech recognition, text-to-audio conversion, and translation, it can be used to create intelligent systems that revolutionize communication and interaction.
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