LLM topic discovery in Virtual Agent
Summarize
Summary of LLM Topic Discovery in Virtual Agent
Large Language Models (LLMs) enhance the Virtual Agent's ability to understand and process user statements in natural language. This allows for more effective and efficient conversations compared to traditional Natural Language Understanding (NLU), requiring less setup time.
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Key Features
- LLM Model Selection: When creating a topic in the Virtual Agent Designer, you can opt for LLM as the model type if Now Assist in Virtual Agent is enabled.
- Topic Discovery: LLMs automatically identify user intents without the need for predefined models, intents, or keywords, streamlining topic creation.
- Entity Extraction: LLMs can extract relevant entity values directly from user input without prior mapping, simplifying the process.
- Topic Switching: Users can seamlessly switch between topics in a conversation without needing to cancel or restart the session.
Key Outcomes
By utilizing LLMs, ServiceNow customers can expect:
- Faster creation and deployment of topics without the complexity of maintaining NLU models.
- Improved user experience through accurate topic discovery and entity extraction.
- Efficient handling of multiple topic switches during conversations, enhancing user interaction.
Large language models (LLMs) enable Virtual Agent to process user statements in plain language. Conversations that use LLMs can outperform those conversations that use Natural Language Understanding (NLU), with easier setup.
How LLMs work in Virtual Agent
When you create a topic in Virtual Agent Designer, you can select LLM as the model type for your topic whenever Now Assist in Virtual Agent is turned on. Virtual Agent then uses LLM generative AI to discover topics that match the user's intent.
Unlike NLU topics, LLMs don't require models, intents, or keywords to be linked to the topic. LLMs can discover topics and perform language-related tasks, such as text generation for case summaries and resolution notes, without months of training on NLU models. Overall, you can create, configure, and deploy LLM topics faster than working with NLU.
With LLMs, Virtual Agent can do the following:
- Perform topic discovery without needing a singular declared intent in a given topic.
- Find intents without backup keywords as in NLU modeling.
- Extract entity values without prior mapping as in NLU modeling.
- Handle multiple conversation topic switches in a single conversation session.
Topic discovery
With LLM topic discovery, topic authors no longer need to create and maintain complex NLU models and intents with backup keywords. The LLM does all of the heavy lifting for you. The only requirement is a robust, plain language topic description on the Properties tab in Virtual Agent Designer. The LLM uses this description to find the best topic match for the user utterance. If there are multiple potential matches, the user will see a list of topics to choose from.
For example, if a user asks Virtual Agent to calculate a ride share fee, the LLM matches the user utterance with an existing topic that can calculate the ride share with a tip.
Entity extraction
With LLM topics, the LLM has all the information needed to determine if an utterance has the information to fulfill a request. Unlike NLU models, you don't need to associate entities with a user input node or add nodeless NLU entities as input variables to a topic. The LLM simply finds the entity that most closely fits the user intent.
Topic switching
Topic switching is faster and easier with LLMs compared to NLU topics. The LLM processes your requests to change intent made in natural language, and activates the appropriate topic.
For example, if you start a conversation by asking for a mobile phone, you don't have to cancel the order first or restart the conversation. Instead, you can ask Virtual Agent to order a laptop instead. Virtual Agent immediately switches from the mobile phone topic to the laptop topic. Topic switching can be done during a query, but not within a catalog ordering flow.