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?
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“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 to generate 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, and 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 make a decision about fine-tuning 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, when 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.