Augment NLP Services
with ChatGPT Integration

What Makes ChatGPT Different from Previous Models

A large language model developed by OpenAI, ChatGPT is based on Generative Pre-trained Transformer architecture, specifically the GPT-3.5 architecture. The model is trained on a massive dataset of text, such as books, articles, and websites, to learn the structure, patterns, and relationships between words, and the nuances of natural language.

Show More

When given a prompt, such as a question, ChatGPT generates a sequence of text in response that is logically in line with the prompt. It does this by using the context of the prompt and the previous words in the generated response to calculate the probability of possible next words. The model then chooses the word with the highest probability as the next word in the sequence.

ChatGPT uses a technique called "attention" to weigh the importance of different parts of the input when making predictions. This allows the model to focus on specific words or phrases in the input when generating a response, helping it understand the prompt's meaning and intent.

• It understands context better, which helps it establish the user’s requirement correctly and provide an accurate response.
• It provides human-like responses to questions, which makes it an ideal model for conversational chatbots.
• It remembers the previous prompts given in the same conversation and can form an answer based on the entire conversation rather than just the latest prompt.
• Its size and complexity make it more capable of diverse and nuanced responses than smaller language models.
• Being a versatile language model, it can be used for a wide range of NLP tasks including chat support, content generation, content summarization, etc.

Show Less

QBurst Services in GPT

QBurst Services in GPT

We draw on the strength of our experience in implementing machine learning applications, including training language models, generating training data using automated mechanisms, and achieving the best results from models. Our experience in language models like BERT, XLNet, RoBERTa, DistillBERT, etc. is also an added advantage.

Our AI team can help you:

• Identify GPT use cases for your business.
• Fine-tune GPT to provide accurate and enhanced results.
• Implement NLP-based applications with GPT models.
• Customize open-source architectures, build inference engines, etc.
• Integrate GPT with your existing applications, tools, or website.

Unlock the Potential of Generative Pre-Trained Transformer

Unlock the Potential of Generative Pre-Trained Transformer

While ChatGPT can enhance your customer service or content creation capabilities, it cannot be fine-tuned for a specific use case. This is where the powerful language model GPT-3 comes into play. It can be fine-tuned and customized for specific use cases and thus advantageously deployed by different verticals for a wide range of requirements.

Through its API, OpenAI has made available a family of models of varying sizes that are trained on different types of data. These models can be used for a variety of tasks like classification, text conversion, summarization, translation, etc. with prompt engineering.

GPT bots do not depend on a rigid question framework to understand user queries but can reply to queries in natural language. Such bots can understand the context and intent of the user better than other models. They can continue a conversation from where it was left off earlier and respond in a conversational tone, unlike traditional chatbots. GPT bots can also respond in different styles and languages. They can understand complex instructions and produce better content.

Fine-Tuning GPT Model Through Transfer Learning

A pre-trained GPT-3 model can be fine-tuned for a specific use case through transfer learning. GPT-3 model has been pre-trained on a substantial amount of text material, enabling it to comprehend the subtleties of language. We can utilize this pre-training and fine-tune the model for tasks, such as text summarization, language translation, and chatbot development. The model is fine-tuned by providing samples of input and the expected output. It is then trained until it can accurately predict the outcome for the given new inputs.