Using Large Language Models to understand natural language

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’re 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.


Bidirectionality vs. Unidirectionality:

  • BERT: Bidirectional - Can process text left-to-right and right-to-left.
  • GPT: Autoregressive and unidirectional - Text is processed in one direction.

Segment of Transformation Model Used:

  • BERT: Uses the encoder segment of a transformation model.
  • GPT: Uses the decoder segment of a transformation model.


  • BERT: Applied in Google Docs, Gmail, smart compose, enhanced search, voice assistance, analyzing customer reviews, and so on.
  • GPT: Applied in application building, generating ML code, websites, writing articles, podcasts, creating legal documents, and so on.

Unsupervised Tasks:

  • BERT: Uses two unsupervised tasks - masked language modeling (fill in the blanks) and next sentence prediction (predicting if sentence B comes after sentence A).
  • GPT: Straightforward text generation using autoregressive language modeling.


In the initial phase of our project, we load a JSON file containing intentsdesigned for the chatbot, to make it suitable for data processing task such astext cleaning, extracting patterns, stop words removal, stemming and organizingthem into structured format.

In the model building phase, we used BERT model specific foradvanced language understanding capabilities. The trained model is evaluated onthe test dataset, and evaluation metrics.

The prototype developed using the provided code offers a foundation for a widerange of applications. By conversion and language understanding capabilitieswith speech recognition, text-to-audio conversion, translation, it can be used to create intelligent systems that revolutionize communication and interaction.

Tags: generative ai, BERT, chatbot, intent detection, large language models

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