Enhancing Intent Classifier Training with Large Language Model-generated Data
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 labeled da...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2414483 |
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| _version_ | 1850064094024957952 |
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| author | Alberto Benayas Sicilia Miguel-Ángel Marçal Mora-Cantallops |
| author_facet | Alberto Benayas Sicilia Miguel-Ángel Marçal Mora-Cantallops |
| author_sort | Alberto Benayas |
| collection | DOAJ |
| description | 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 labeled data, as acquiring substantial, annotated datasets is costly and impractical. Data augmentation addresses this by expanding datasets with modified or synthetic examples, a common practice in computer vision but more complex in NLP due to the discrete nature of language. Traditional NLP data augmentation methods have been explored but exhibit limitations. This paper investigates the use of Large Language Models for generating labeled data to enhance intent classification. We explore whether LLM-generated data can effectively augment training sets, comparing its impact on intent classifier performance against traditional augmentation methods. Our study reveals that LLMs can generate diverse and realistic data, potentially improving classifier accuracy in low-data scenarios, thereby providing valuable insights into leveraging generative AI for NLP tasks in real-world applications. |
| format | Article |
| id | doaj-art-0fca1df90e344dc8bdc93ab9ecc97254 |
| institution | DOAJ |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-0fca1df90e344dc8bdc93ab9ecc972542025-08-20T02:49:23ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2414483Enhancing Intent Classifier Training with Large Language Model-generated DataAlberto Benayas0Sicilia Miguel-Ángel1Marçal Mora-Cantallops2Computer Science, University of Alcalá, Alcalá de Henares, SpainComputer Science, University of Alcalá, Alcalá de Henares, SpainComputer Science, University of Alcalá, Alcalá de Henares, SpainIntent 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 labeled data, as acquiring substantial, annotated datasets is costly and impractical. Data augmentation addresses this by expanding datasets with modified or synthetic examples, a common practice in computer vision but more complex in NLP due to the discrete nature of language. Traditional NLP data augmentation methods have been explored but exhibit limitations. This paper investigates the use of Large Language Models for generating labeled data to enhance intent classification. We explore whether LLM-generated data can effectively augment training sets, comparing its impact on intent classifier performance against traditional augmentation methods. Our study reveals that LLMs can generate diverse and realistic data, potentially improving classifier accuracy in low-data scenarios, thereby providing valuable insights into leveraging generative AI for NLP tasks in real-world applications.https://www.tandfonline.com/doi/10.1080/08839514.2024.2414483 |
| spellingShingle | Alberto Benayas Sicilia Miguel-Ángel Marçal Mora-Cantallops Enhancing Intent Classifier Training with Large Language Model-generated Data Applied Artificial Intelligence |
| title | Enhancing Intent Classifier Training with Large Language Model-generated Data |
| title_full | Enhancing Intent Classifier Training with Large Language Model-generated Data |
| title_fullStr | Enhancing Intent Classifier Training with Large Language Model-generated Data |
| title_full_unstemmed | Enhancing Intent Classifier Training with Large Language Model-generated Data |
| title_short | Enhancing Intent Classifier Training with Large Language Model-generated Data |
| title_sort | enhancing intent classifier training with large language model generated data |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2414483 |
| work_keys_str_mv | AT albertobenayas enhancingintentclassifiertrainingwithlargelanguagemodelgenerateddata AT siciliamiguelangel enhancingintentclassifiertrainingwithlargelanguagemodelgenerateddata AT marcalmoracantallops enhancingintentclassifiertrainingwithlargelanguagemodelgenerateddata |