How Open AI Fine-Tuning Can Turn a Conversational Chatbot Into a Worthy Alternative to a Human Coach
According to Statista, the global chatbot market size is estimated to be $0,84 billion in 2022, and it is expected to be around $4,9 billion by 2032. The growth rate of this area can reach 19,29% by 2032. Sound amazing? But what business goals do companies pursue while deciding to build chatbots?
Chatbots are fast becoming a business imperative for businesses that want to engage with their customers. Online chat through chatbots has grown faster than any prior channel, stated Eileen Brown, Digital Marketing Consultant at ZDNet. That’s why this is a chance for companies to revolutionize customer relationships and offer better support to employees. But how to make a truly smart chatbot?
No doubt, fine-tuning GPT-3 is a way to turn a chatbot into a sophisticated and intelligent virtual assistant that can be a worthy alternative to a human. Would you like to know how to do this? Keep reading and you’ll find out how to fine-tune GPT-3 with benefits for your business.
What is GPT-3?
GPT-3 has come into vogue so fast and created so much noise that we cannot stay indifferent. We can admire its capabilities, we can criticize it. But we can’t reject the fact that this is a great technology that is going to transform the business world by achieving human-like intelligence.
Having 175 billion parameters and an in-built Generative Pre-trained Transformer module, GPT-3 is capable of generating large volumes of sophisticated and relevant machine-generated text as a response to a small amount of input text. What’s more, it can have a wide range of applications, including generating text, recognizing emotions, language modeling, and language translation for chatbots.
How does the GPT-3 chatbot work?
The most popular use of GPT-3 is creating a highly capable and intelligent chatbot ChatGPT which can engage in human-like conversation. What’s so special about it? The GPT-3 chatbot can comprehend human speech and react to it, generate predictions, recognize emotions and contexts, write essays, translate texts, and summarize. But before going to the task of how to fine-tune your chatbot with GPT-3, let’s try to understand how a GPT-3 chatbot works.
The peculiarity of a GPT-3 chatbot is that it can work without the Internet. It is a language processing system that has a base of knowledge that doesn’t require a connection to the Web. The advantage of such work is that a chatbot can work online. But every class has a silver lining. So, the bad news is that the database can be outdated as it contains information only until 2021.
So you’re aware of the capabilities of GPT-3 and understand how a GPT-3 chatbot works. Now our further plan of action is to clarify what fine-tuning for chatbots based on GPT-3 is needed. Let’s start.
Why is fine-tuning for chatbots based on GPT-3 needed?
Would you like your chatbot to be intelligent and sophisticated? Then fine-tuning based on GPT-3 is just for you. It will improve the performance of your chatbot and make it more accurate and efficient in responding to your business needs. See what benefits you can get if you fine-tune your chatbot with GPT-3:
First, GPT-3 fine-tuning improves chatbot response relevance, accuracy, and responsiveness to specific situations. What’s more, GPT-3 contributes to better comprehension and analysis of context by a chatbot. As a result, this chatbot can provide a more natural and human-like conversation that boosts customer engagement and experience.
Second, fine-tuning with GPT-3 makes chatbot development more cost-effective and efficient. It reduces the time and costs necessary for building a chatbot, allowing developers to use pre-existing models and not create a chatbot from scratch. That’s why, this is a chance for your business to reduce time and effort by doing just chatbot improvement based on GPT-3.
Third, GPT-3 fine-tuning enhances the natural language processing capabilities of the chatbot. As a result, a chatbot can better analyze and understand natural language and offer more accurate responses. What you can get from this is the possibility to suggest a more personalized conversation to your customers.
Fourth, fine-tuning with GPT-3 will turn a traditional chatbot that can generate only simple responses into a smart one with the ability to handle more complex queries. As a result, you can get an improved chatbot that can cope with complicated and nuanced tasks and generate responses free from biases and misinformation.
So see, how many benefits you’ll get if you decide to fine-tune your chatbot with GPT-3. But how does fine-tuning with GPT-3 work in practice? Hope, Wetelo’s case will help you get a real picture.
How did Wetelo fine-tune the GPT-3 chatbot?
At Wetelo, we have expertise in fine-tuning the GPT-3 chatbot to share with you. Our client came to us with a request to improve their chatbot to recognize users’ emotions and make the chatbot more intelligent and sophisticated. That’s why, the best way to do this was to fine-tune their chatbot with GPT-3 conversational module. So see, how we did it.
Steps to fine-tune chatbot with GPT-3
Now, that you know how to benefit from fine-tuning GPT-3, it’s high time to explain what steps we used to improve our client’s chatbot.
- Step 1. Select the correct training data. Our first task was to choose several scenarios of conversation created by coaches and save them in the database to be used by the chatbot. This step helped us to diversify the bot’s answers and make the conversation between a chatbot and the user more natural.
- Step 2. Deciding on fine-tuning method. We used few-shot learning as we needed a few possible scenarios of one response. But if you just want to improve the performance of your chatbot, you can use transfer learning. If you don’t have training data, you can choose zero-shot learning.
- Step 3. Choosing a GPT-3 model. Our next task was to test and compare GPT-3 models like Davinci, Ada, and Curie and select the best option according to the client’s needs. We have chosen Davinci as it was also the best model in terms of understanding the text intent, which was crucial for our client’s solution.
- Step 4. Developing a conversational module. Then we built a module that would receive messages from employees and reply to them with text generated by GPT-3. With this module, the chatbot could understand employees’ moods, paraphrase employees’ messages, and express empathy.
- Step 5. Conducting sentiment analysis. Finally, we conducted sentiment analysis to check whether GPT-3 can define the characteristics of emotions. For this task, our team used sentiment-roberta-large-english model and Huggiing Face API. After testing, we concluded that GPT-3 worked as intended, as it was able to distinguish negative sentiment from neutral.
All these steps have helped Wetelo to fine-tune our client’s chatbot with GPT-3. So we transform a traditional chatbot into a GPT-3-based one that can recognize emotions, understand the text intent, and respond to users with empathy.
Why is the properly fine-tuned GPT-3 chatbot becoming an essential tool for business?
The properly fine-tuned GPT-3 chatbot is a road to many business transformations and improvements. This powerful tool enables changes that take companies to the next level and introduce quality SaaS solutions. Let’s see how fine-tuning your chatbot with GPT-3 can drive your business.
As GPT-3 fine-tuning enhances the chatbot’s natural language processing capabilities, it makes a chatbot more intelligent and responsive. As a result, it can increase customer satisfaction and engagement by providing more accurate responses and handling more complex queries.
When a chatbot is fine-tuned with GPT-3, it can become a wonderful alternative to humans. For example, the improved chatbot can reduce the workload on the customer support team by handling more complex queries. That’s why customers can fail to recognize whether they are talking with a human or a machine.
Finally, a fine-tuned GPT-3 chatbot can increase sales by processing more customers’ requests and boosting the quality of customer support service. See how it works. When the number of clients is increasing, a company can deal with challenges how to respond to them quickly and manage all their requests at the same time. In this case, a fine-tuned GPT-3 chatbot can take it on its shoulders. As a result, employees can focus on other tasks, a chatbot is responsible for conversation with clients, and the company gets satisfied customers and increases sales.
Our client’s success story is an example of how fine-tuning with GPT-3 can drive business growth. After we improved the chatbot by creating a conversational GPT-3 module, the company got a refined, smart, and sophisticated alternative to a human. This virtual coach contributes to change implementations among companies, offering support to employees and preventing possible risks associated with employee burnout and turnover. As a result, this GPT-3 chatbot is widely used by top companies that implement and manage changes.
Looking to fine-tune a GPT-3 chatbot? Trust this matter to the Wetelo team
See with GPT-3 the future of the chatbot market is more promising and optimistic as this technology offers new opportunities for businesses, especially in terms of customer and employee support. No doubt, fine-tuning with GPT-3 is a challenging task. But the risk is noble.
If you are still confused about what GPT-3 model to choose, feel free to read our related article and catch some inspiration for fine-tuning. And when you need some additional help, Wetelo can assist you. Having expertise in fine-tuning chatbots with GPT-3, we can share this challenging path and offer you a brilliant product. Just get in touch with us, and we are always at your service.
Frequently Asked Questions
Can GPT-3 be fine-tuned?
GPT-3 can be fine-tuned to show better outcomes in terms of processing human language and learning new patterns and structures of the task. With fine-tuning GPT-3 can achieve better relevance, accuracy, and performance, especially when it comes to sophisticated tasks.
How to fine-tune OpenAI GPT-3?
To fine-tune OpenAI GPT-3, it is necessary to do the following steps: collect data, prepare dataset, choose GPT-3 language model: Curie, Davinci, Babbage, or Ada, build the model, fine-tune, evaluate the model, deploy the model. Good luck, you can do it!