Large Language Models (LLMs) have introduced more human-like and contextually aware interactions, allowing developers to build sophisticated chatbots with minimal effort. This innovation reduces the need for extensive rule-based programming and enables rapid deployment across various applications. However, there are challenges…
The image above outlines the various elements and features constituting a Large Language Model (LLM).
The challenge lies in accessing each of these features at the appropriate time, ensuring stability, predictability, and, to a certain extent, reproducibility.
Many organisations and technology providers are navigating the transition from traditional chatbots to incorporating Large Language Models with varying levels of success.
✨ Traditional Chatbot IDEs
Traditional chatbots typically consist of four basic elements:
- Intent Detection (NLU)
- Entity Extraction (NLU)
- Response Messages (Message Abstraction Layer)
- Dialog-turn/Conversation State Management (Dialog Flow Control)
Recently, numerous attempts have been made to reimagine this structure, aiming to loosen the rigidity of hard-coded and fixed architectural components.
✨ Natural Language Understanding (NLU)
The NLU engine is the only “AI” component of the chatbot, responsible for detecting intents and entities from the input.
It includes a GUI for defining training data and managing the model.
The typical advantages of NLU engines are:
- Numerous open-source models.
- Small footprint and not resource-intensive, making local and edge installations feasible.
- No-code UIs.
- Extensive corpus of named entities due to long-term usage.
- Predefined entities and training data for specific verticals, such as banking, help desks, HR, etc.
- Rapid model training, with the ability to train models multiple times per day in a production environment.
- Initial introduction of LLMs for generating NLU training data.
- Use of LLMs to generate training data for NLU models based on existing conversations and sample data.
✨ Conversation Flow & Dialog Management
The dialog flow and logic are designed and built within a no-code to low-code GUI.
The flow and logic follow a predefined path with specific logic points.
The conversation progresses based on input data matching certain criteria at these logic gates.
Efforts have been made to introduce flexibility to the flow, aiming to add some semblance of intelligence.
✨ Message Abstraction Layer
The message abstraction layer holds predefined bot responses for each dialog turn. These responses are fixed, with templates sometimes used to insert data and create personalised messages.
Managing these messages becomes challenging as the chatbot application grows, and the static nature of the messages can lead to a significant total number.
Introducing multilingual chatbots adds considerable complexity. Whenever the tone or persona of the chatbot needs to change, all of these messages must be revisited and updated.
This is also one of the areas where LLMs were first introduced to leverage the power of Natural Language Generation (NLG).
✨ Out-Of-Domain
Out-Of-Domain questions are handled by knowledge bases & semantic similarity searches.
Knowledge bases were primarily used for QnA and the solutions made use of semantic search. In many regards this could be considered as an early version of RAG.
In conclusion, the integration of Large Language Models (LLMs) into chatbot development frameworks marks a significant leap forward in creating more human-like and contextually aware interactions.
By reducing the reliance on rigid, rule-based programming, LLMs enable developers to build sophisticated chatbots with greater ease and speed.
However, this transition is not without its challenges.
Accessing and effectively utilising the various features of LLMs while ensuring stability and predictability remains a critical concern.
Organisations and technology providers are actively navigating these complexities as they embrace LLMs, each with varying degrees of success.
As innovations in Natural Language Understanding (NLU) and Natural Language Generation (NLG) continue to evolve, the future promises even more seamless and intelligent chatbot interactions, reshaping how we interact with technology in diverse applications.
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I’m currently the Chief Evangelist @ Kore AI. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.
