Intro
AI powered chatbots are an increasingly popular way for organisations to automate customer support or other services as they strive to create 24/7 and ‘always-on’ digital experiences. The rise of generative AI now means that the AI technology behind chatbots won’t always be the same. There are now two broad approaches: natural language processing (NLP) chatbots or large language model (LLM) powered chatbots.
In this blog, we explore what are the key differences between the two and which may be better for your needs.
NLP Chatbot Pros:
- More mature technology – NLP chatbots have been around for several years now
- Easy to control/customise – These bots can be trained on very specific use cases to help you create very useful solutions.
- Cost effective – can get up and running with relatively low costs
- Easy to run – Lower computational power needed compared to LLM bots.
NLP Chatbot Cons:
- Less conversational – Limited ability to handle nuanced conversations. Relies on scripted responses.
- Requires regular training – NLP bots need lots of training data inputted and regular tweaking of this content.
- Prone to misunderstanding – struggles with conversations outside of training data.
LLM Chatbot Pros:
- Natural conversations – More natural, contextual conversations. Can adapt to topics not in training data.
- Scale – Ability to understand and generate nuanced responses at a huge scale.
- More human-like – Conversations feel more like you are having a real dialogue.
- Knowledge retrieval – Can pull information from large datasets or knowledge stores to answer questions
LLM Chatbot Cons:
- Still early stages – LLMs and generative AI is still an emerging technology that lacks enterprise maturity
- Inherent risk – Potential for generating biassed, incorrect or inappropriate content.
- Harder to run – Requires large computational resources and private LLMs can be very costly.
- Lack of explainability – Hard to decipher where the chatbot got its answer from.
- Less control – you cannot directly control what a chatbot will respond with compared to the NLP chatbot.
Summary
Overall, NLP chatbots offer a more established option for narrow use cases where the accuracy of information is paramount. LLM chatbots provide a better user experience, allowing for free-flowing conversations thanks to their generative capabilities. But LLMs come with higher risks around the accuracy of the content.
The hybrid approach
As both technologies continue to evolve, we believe that hybrid approaches, combining NLP and LLM together, may offer the best of both worlds. For example, you could use an NLP based bot responses but fallback to an LLM bot if you can’t recognise the intent. Or you could use an LLM to better classify a user input before sending it to your NLP chatbot.
The needs of your organisation should ultimately drive which solution is the best for you to move forward with.
At EBM we have been helping our customers ensure they have the right solutions in place so that they can improve their conversational AI offering and make the most of these emerging technologies safely and securely.
