A study recently published in the journal Research Methods in Applied south korea phone number library Linguistics found that even linguistic experts were only able to distinguish between scientific texts created by AI and humans 39% of the time.
But does this mean that artificial intelligence can communicate like a human in online correspondence? Developers of chatbots based on generative text neural networks believe that it can, but it shouldn’t .
The point is that excessive imitation of a person can lead to the emergence of the “uncanny valley” effect - aversion to artificial entities that are slightly different from living subjects. Instead, it is proposed to make chatbots less robotic .
Using Chat GPT in creating chatbots allows you to not only bring such communication indicators as creativity, understanding of context and naturalness of conversational flow closer to human ones, but also help achieve the main goal - improving customer service - in other ways.
Let's talk about how else the introduction of a text neural network into chatbots can be useful:
Customer sentiment monitoringLarge language models (LLM) are created based on neural networks, which can be useful for assessing customer satisfaction after each response in a chatbot. They can even recognize sarcasm! If customer satisfaction remains low after several bot responses in a row, the system automatically connects a live interlocutor to the conversation.
Bringing the conversation back to the topic of conversationConversations with customers do not always follow the logical route of a chatbot, as live participants in the conversation can change the topic of conversation and ask unexpected questions. A neural network can help bring the conversation back on track by referring to an earlier stage where the request to the system was formulated. This helps to give answers on the merits and lead the conversation to the desired result for the client.
Creating Lexicons for the Service SectorA neural network can be used to create a lexicon - a set of commonly used symbols and expressions that businesses embed in their bots so that they understand the jargon of customers and employees. The lexicon can cover anything from abbreviated terms for tests to airport codes. It can then be embedded in chatbots so that they understand the customer at a glance.
Hints for live chat agentsAs we know, in difficult cases, animated employees come to the aid of the chatbot. To speed up the process of inclusion in the dialogue, the neural network can offer hints to employees based on the semantic match of the client's request and the available knowledge base, product manuals and Internet search with a link to sources.
Mapping the customer journey Using neural networks, you can automatically reduce the history of communication with a client to a few key points of the conversation, and then upload this information to the CRM with a note about the dialogue status for a better understanding of the customer journey. Subsequently, you can summarize this data to clarify at what stage the most questions arise in order to improve the experience of interaction with the company.
