Enhancing Intent Classifier Training with Large Language Model-generated Data

<p>Intent classification is essential in Natural Language Processing, serving applications like virtual assistants and customer service by categorizing user inputs into predefined classes. Despite its importance, the effectiveness of intent classifiers is often constrained by the scarcity of l...

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Bibliographic Details
Main Author:	Alberto Benayas (19841270) (author)
Other Authors:	Sicilia Miguel-Ángel (19841273) (author), Marçal Mora-Cantallops (19841276) (author)
Published:	2024
Subjects:	Genetics Immunology Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified traditional augmentation methods leveraging generative ai explore whether llm categorizing user inputs natural language processing large language models large language model intent classifier performance data augmentation addresses enhance intent classification generating labeled data intent classification labeled data intent classifiers realistic data generated data data scenarios synthetic examples study reveals predefined classes paper investigates often constrained nlp tasks nlp due generate diverse expanding datasets exhibit limitations discrete nature customer service computer vision common practice annotated datasets acquiring substantial
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Enhancing Intent Classifier Training with Large Language Model-generated Data

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