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Chatbots of tomorrow:
The evolution of
Large Language Models in AI

Learn how to harness the power of our new OpenAI feature, and improve the quality of your chatbot responses.

What are Large Language Models?

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The term "large" refers to the scale and complexity of these models. They are typically trained on extensive datasets containing diverse text from the internet, books, articles, and other sources. This extensive training enables them to learn the patterns, grammar, and semantics of human language.

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Large language models have a wide range of applications, including natural language processing (NLP) tasks such as text generation, completion, classification, translation, and question-answering. They can generate coherent and contextually appropriate text, understand complex language structures, and respond to queries in a human-like manner.

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Some  Large Language Models

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Quick Overview

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Human-Like Chatbots: Personalized Conversations

Improved Understanding ​

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LLMs have a deeper understanding of context and semantics, enabling chatbots to comprehend user queries more accurately. This results in more relevant responses and reduces instances of misinterpretation

Efficiency and Scalability​

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Integrating an LLM automates the chatbot's response generation process, making it more efficient and scalable. Chatbots can handle a larger number of users simultaneously without compromising the quality of responses.

Multilingual Capabilities

 

LLMs are capable of understanding and generating text in multiple languages. Integrating an LLM with a chatbot allows it to communicate effectively with users from diverse linguistic backgrounds.

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Large Language Models
Large language models (LLMs) like GPT-3 and Claude represent a recent advancement in natural language processing using massive neural networks trained on huge text datasets (GPT-4 trained using 1.7 trillion parameters). By ingesting these parameters, LLMs learn sophisticated linguistic representations. This allows them to generate surprisingly human-like text and engage in conversational dialog with memory.
LLMs can be fine-tuned with custom data to enable conversational AI applications like chatbots. Their natural language capabilities empower capabilities like dynamically generating responses, summarizing content, and extracting information for agents. LLMs also allow for more natural dialog directly between the user and underlying knowledge.
Key applications enabled by LLMs include:
- Conversational AI for chatbots and voice assistants
- Automated content generation
- Text summarization
- Data extraction from unstructured text
- Contextual, human-like dialog
Natural Language Processing 
Natural language processing (NLP) is an artificial intelligence capability that allows computers to understand, interpret, and generate responses to human language input. Key NLP techniques like intent classification, entity extraction, sentiment analysis, dialogue management, and speech recognition empower applications such as:

- Chatbots and virtual assistants capable of natural conversation
- Validation of input data
- Structuring unstructured text data
- Internal chatbots to assist employees
NLP applies machine learning to linguistic data to empower more natural human-computer conversations and language understanding.

The Difference between LLMs and NLPs

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Possible Use Case

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Call Centre:
Customer Service.
- Users calling several times for assistance with tracking information.
 
How can AI help both the user and the agent?
Scale
LLMs can handle large volumes of customer inquiries simultaneously. 

Hundreds of conversations can be managed in parallel without long wait times or dropped connections.
OpenAI + Gotbot are built for scale
Efficiency and effort reduction
LLMs can quickly parse through databases and documentation to find relevant information.

A human agent might need to search through multiple knowledge bases to find answers, while an LLM can instantly pull the right data. 
This allows customer issues to be resolved faster.
Knowledge Base
Agent
One major advantage of using LLMs for customer service is their ability to 
understand nuanced customer requests and generate thoughtful responses.
User
LLMs can also generate content on-the-fly to address customer questions. 
For example, they can create customised tutorials, how-to guides, or FAQs tailored to the user’s specific needs. 
The content is high-quality since it’s generated from the LLM’s vast training data.
Sentiment
Agent
One major advantage of using LLMs for customer service is their ability to 
LLMs can summarize key points and sentiment from lengthy conversations efficiently. This saves time compiling reports.
Summarization: LLMs can summarize key points and sentiment from lengthy conversations efficiently. This saves time compiling reports.
generate thoughtful responses.
User
Sentiment classification: LLMs can be fine-tuned to categorize the sentiment of customer utterances as positive, negative or neutral. This provides an overview of how customers feel about their service experience.

Want to know how the LLM can work for your business?

Contact us for more information on how to get started.
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