2. Limited scalability
Intent classifiers are also not designed to scale. Adding new intents means collecting more data and retraining the model, which quickly becomes a development bottleneck. Plus, they can be a maintenance headache, because as language use evolves, so do the statements.
3. Poor understanding of the language
intent classifiers lack a true understanding of language. They have difficulty with language variations, such as:
synonyms
paraphrase
ambiguous wording
typos
unknown colloquial expressions
fragmented entries
They also tend to process each utterance in isolation, meaning they lack the ability to maintain context throughout a conversation.
4. Overfitting
Intent classifiers are prone to overfitting as they spain phone numbers memorize training examples instead of learning general patterns.
This means they work well with exact phrases they have seen, but have difficulty with new or varied input. This makes them much more fragile than is appropriate for professional use.
6 reasons why llms is better
llms have practically solved these problems. They understand context and nuances, and developers don't need to fill them with training data to get them up and running. An llm-based agent can start chatting from the moment it is created.
1. Shotless learning ability
llms do not need examples to learn. Thanks to their extensive prior training, they already understand the context, nuances, and intent without needing to be given concrete examples by developers.
2. A little thing called nuance
llms highlight where intent classifiers fall short. They can interpret clichés, sarcasm and ambiguous language with ease.
Their extensive training on diverse data sets allows them to capture the subtle nuances of human communication that intent classifiers often miss.
3. Better context
llms do not lose the thread of the conversation. They remember what was said before, which makes interactions flow naturally and seem more coherent.
This context also helps them clarify ambiguities. Even when the information is vague or complex, they can reconstruct it by analyzing the conversation as a whole.
4. Scalability
llms are 100% better at scale. They do not need recycling to address new topics, thanks to their extensive knowledge of the language.
In this way, they are prepared to manage practically any use case from the first moment. For multi-agent systems, it is obvious to use llm instead of an intent classifier.
5. Flexibility
llms are not based on rigid templates. Its flexibility makes the responses natural, varied and perfectly adapted to the conversation. They are a much better experience for users than fragile intent classifiers.
6. Less training data
llms do not need task-specific tagged data to do their job. Their power comes from massive pre-training on diverse texts, so they do not depend on carefully annotated data sets.
If necessary, developers can always customize llm for their project. For example, llms can be tuned with minimal data, so it can be quickly adapted to specialized use cases or industries.
