Will Models like ChatGPT completely Replace Chatbots?

Just an excerpt of a larger article,  struck me as I  looked at the overall experience in ChatGPT, well put.  Clickthrough for much more.  I am now putting together a means of classifying related search for a proposed application. 

Will models like ChatGPT completely replace chatbots?   in Venturebeat

OpenAI opened the ChatGPT beta in late November 2022, in a move that produced the most powerful natural language processing (NLP) AI model to date. It quickly went viral, attracting a million users in the first five days.  

The underlying premise of this question is whether large language models (LLMs) like ChatGPT will transform the reputation of chatbots from clunky, impersonal and faulty into algorithms so meticulous that (a) human interaction is no longer needed, and (b) traditional ways of building chatbots are now completely obsolete. We’ll explore these premises and give our view on how ChatGPT will impact the CX space.

Broadly speaking, we differentiate between conventional chatbots and chatbots like ChatGPT built on generative LLMs.

Conventional chatbots

This category includes most chatbots you’ll encounter in the wild, from chatbots for checking the status of your DPD delivery to customer service chatbots for multinational banks. Built on technologies like DialogFlow, IBM Watson or Rasa, they are limited to a specific set of topics and are not able to respond to inputs outside of those topics (i.e. they are closed-domain). They can only produce responses that have been pre-written or pre-approved by a human (i.e. they are non-generative).

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LLM-based chatbots

These can respond to a wide range of topics (i.e. they are open-domain) and generate responses on the fly, rather than just selecting from a pre-written list of responses (i.e. they are generative). They include Google Meena, Replika.ai, BlenderBot, ChatGPT and others.   

LLM-based chatbots and conventional chatbots fulfill somewhat different purposes. Indeed, for many CX applications, LLMs’ open nature is less help and more hindrance when building a chatbot that can specifically answer questions about your product or help a user with an issue they’re experiencing. 

Realistically, LLMs won’t be let loose into the CX domain tomorrow. The process will be much more nuanced. The name of the game will be marrying the expressiveness and fluency of ChatGPT with the fine-grained control and boundaries of conventional chatbots. This is something that chatbot teams with a research focus will be best suited for.

Where can you already use ChatGPT today when creating chatbots?

There are many aspects of chatbot creation and maintenance that ChatGPT is not suited for in its current state, but here are some for which it is already well-suited:

Brainstorming potential questions and answers for a given closed domain, either on the basis of its training data, or fine-tuned on more specific information — either by OpenAI releasing the ability for fine-tuning when ChatGPT becomes accessible by API, or through including desired information via prompt engineering. (Caveat: It is still difficult to know with certainty where a piece of information comes from, so this development process will continue to require a human in the loop to validate output.)

Training your chatbot: ChatGPT can be used to paraphrase questions a user might ask, particularly in a variety of styles, and even generate example conversations, thereby automating large parts of the training. 

Testing and QA. Using ChatGPT to test an existing chatbot by simulating user inputs holds much promise, particularly when combined with human testers. ChatGPT can be told the topics to cover in its testing, with different levels of granularity, and, as with generating training data, the style and tone it uses can be varied.

We see the next generation of CX chatbots continuing to be based on conventional, non-generative technology, but generative models being used heavily in the creation process. ...  '