Polyglot GPT Engine: Generative Pre-Trained Transformer Based Multilingual Chatbot

Smart overview of the Invention

A novel multilingual chatbot system combines a semantic translation engine with a Generative Pre-Trained Transformer (GPT) model. This integration allows users to engage in natural language conversations in their preferred language, breaking down language barriers and facilitating seamless knowledge exchange. The system translates user input into English, generates a response using the GPT model, and then retranslates the response back to the user's original language.

System Architecture

The system architecture consists of several key components as depicted in FIG. 1. These include:

• User Interface: A multilingual interface for user input and response delivery.
• Query Function Module: Processes user input in various languages.
• Semantic Engine: Translates non-English input into English using machine learning algorithms.
• GPT Model: Generates responses based on translated English input.
• Response Retrieval API: Fetches the generated response from the GPT model.
• Response Translation Module: Re-translates the response back to the user's original language.
• Output Interface: Delivers the final translated response to the user.

Functional Flow

The chatbot system operates through a series of steps illustrated in FIGS. 2, 3, and 4:

• Step 1: User inputs a query in their preferred language via the User Interface.
• Step 2: Semantic Engine translates the input into English.
• Step 3: GPT model processes the translated text to generate a response.
• Step 4: Response is retrieved through the Response Retrieval API.
• Step 5: Response is re-translated into the original language using the Semantic Engine.
• Step 6: Translated response is delivered back to the user through the Output Interface.

Existing Solutions and Drawbacks

GPT excels at understanding and generating human-like text, leveraging pre-training on large datasets to handle complex language tasks. However, it faces challenges such as potential factual inaccuracies, sensitivity to input phrasing, and biases from training data. These issues can lead to incorrect answers or inconsistent outputs.

Proposed Enhancements

To address existing drawbacks, enhancements include expanding cultural and linguistic diversity, increasing language support for global adaptability, improving handling of informal language and regional dialects, and optimizing response time. Additionally, implementing feedback systems can facilitate continuous improvement of the chatbot's performance.