Conversational AI with Large Language Models

OpenAI’s ChatGPT crossed more than 1 million users within a week. This is a testament to the immense potential of powerful language models. ChatGPT is currently available for research preview, but numerous experimental features were introduced using the GPT3.5 base model architecture. These advancements are propelling the transformation of conversational AI towards a human-like experience.

The recent advancements in technology have made large language models (LLMs) a crucial factor in the development of Conversational AI. These models are trained to handle massive amounts of data and generate text with a level of accuracy that rivals that of humans. In this blog, we delve into how LLMs allow machines to comprehend customer messages and assist them in resolving their issues through conversation, making them invaluable digital customer experience tools.

What are Large Language Models?

Large language models (LLMs) are AI systems that are trained on vast amounts of data available on the internet. For instance, ChatGPT was fed a whopping 300 billion words and 570GB of data sourced from books, Wikipedia, research articles, web texts, websites, and other forms of content and writing found online [Ref 1]. 

The promise of large language models (LLMs) lies in their ability to understand and gain knowledge from massive amounts of data and generate text that is highly coherent and natural in context. As these models grow in size and complexity, their knowledge and performance capabilities across multiple AI tasks also increase, delivering ever-higher accuracy.

These models can be categorized into three buckets-

1. Closed system

   Only the functional capabilities of these models are made available to the public, allowing users to interact with their features. Examples include Deepmind Sparrow, Facebook Galactica, ChatGPT, Github Copilot, and Google’s Bard, among others.

2. Semi-open

   Typically, these models can be used for training and inference through APIs. Examples include the GPT-3.5 from OpenAI and the Alexa teacher Model in the Amazon Web Services (AWS) environment. While the architecture of some models may be disclosed, they may not be available for self-hosting.

3. Open-sourced

   The research teams have made these large language models accessible to the public, allowing for inference and training through self-hosting. They are also benchmarked against the latest state-of-the-art models, such as DialoGPT, Godel, DeBERTa from Microsoft, RoBERTa from Facebook, and BERT from Google. These models can be fine-tuned to achieve optimal performance for a specific task.

Benefits of using LLMs

Large language models (LLMs) can significantly enhance the performance of conversational AI platforms through their ability to comprehend natural language and generate a natural response. This makes them an ideal choice for data scientists looking to build advanced conversational AI systems. The key benefits of using LLMs include the following: 

1. Improved communication efficiency with businesses as customers is more likely to receive information in a natural and contextually relevant manner.

2. A head-start in training virtual assistants as LLMs can interpret natural language.

3. The ability to be fine-tuned for specific business contexts by leveraging information from websites, knowledge bases, and other relevant documents.

4. A shorter training cycle for virtual assistants as LLMs already can understand natural language and produce human-like responses.

5. In customer support, large language models assist human representatives by handling routine tasks, freeing up their time to focus on more complex and critical customer issues. This leads to time and cost savings while still maintaining high-quality customer service.

Limitations

Large language models (LLMs) have certain limitations when implemented in a business setting. In the following section, we will delve into how Netomi AI addresses these major limitations. 

1. One of the challenges with LLMs is the potential for incorrect and misleading outputs to customers. This can occur when the information is presented naturally and convincingly, making it difficult to detect errors.

2. Using self-hosted LLMs requires a considerable amount of resources and a complex infrastructure to support the conversational AI setup.

3. Like many AI models, LLMs are limited by the training data it is exposed to. The limitations in the training data can negatively impact the model’s performance.

Netomi harnesses the Large Language Models to automatically and effortlessly build high-quality chatbots customized to businesses.

Netomi’s Instant Bot powered by LLMs

Netomi users can build chatbots for support almost instantly with the help of Knowledge Base, such as websites, documents, historical customer conversations, or databases. 

Netomi AI consumes this knowledge base and fine-tunes the LLMs (e.g., GPT-3.5) based on the context of your business. This enables the conversational AI model’s ability to understand the business context over the knowledge of the natural language and allows it to respond to customer messages seamlessly with the resolution. 

We observed that Netomi’s instant bot could handle up to 60% of conversations automatically with its ability to interpret the knowledge base and present relevant information to the customers.

As mentioned earlier, current Large Language Models suffer from generating deceptive responses with high confidence. At Netomi, we have designed our AI such that the output generated by the LLMs is validated by another state-of-art AI Response Validation model against the knowledge base sources. 

Netomi’s Response Validation model discriminates the responses with factually incorrect information and discards most hallucinating responses.

This additional AI model, on top of Large Language Models, ensures that the customer receives responses that are factually correct and customized to the context of their message. 

We observed that Large language models are computationally expensive, which may add high costs to host these models and also introduce the delay in responding to customer messages. This issue is further amplified when the customer is engaged on voice-based channels.  

At Netomi, we resolve this issue by adding a caching layer that improves the latency when customers ask similar questions.

Caching LLMs’ responses help us to respond three times faster and save 40% of computing resources while ensuring that we provide highly engaging generative responses to all the customers.

Future Direction for Conversational AI and LLMs

LLMs are significantly contributing to the success of Conversational AI technology. We will see even more sophisticated use cases with the latest advancements in LLMs. We are likely to see advanced –

1. Virtual Assistants can handle a broader range of tasks & requests with little or no effort in training & configurations.

2. Highly sophisticated UX where VA generates the responses based on customer message context, emotions, and customer’s previous chat history.

3. Virtual Assistants become more fluent and coherent in their responses based on the conversation context.

4. LLMs shorten the deployment cycle for businesses by providing extraordinary time-to-value for using AI for Conversational AI use cases.

Ref: 

[1] https://analyticsindiamag.com/behind-chatgpts-wisdom-300-bn-words-570-gb-data/#:~:textAccording%20to%20an%20article%20published,and%20writing%20on%20the%20net.