A comparative analysis of encoder only and decoder only models in intent classification and sentiment analysis: navigating the trade-offs in model size and performance.
Intent classification and sentiment analysis stand as pivotal tasks in natural language understanding (NLU), with applications ranging from virtual assistants to customer service. The advent of transformer-based models has significantly enhanced the performance of various NLP tasks, with encoder-onl...
| Published in: | Language Resources & Evaluation Vol. 59; no. 3; pp. 2007 - 2031 |
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| Main Authors: | , , |
| Format: | Article |
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Springer Nature
Sep2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909056&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909056 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909056 10.1007/s10579-024-09796-y ppf: 2007 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1009KB tig: atl: A comparative analysis of encoder only and decoder only models in intent classification and sentiment analysis: navigating the trade-offs in model size and performance. aug: au: Benayas, Alberto Sicilia, Miguel Angel Mora-Cantallops, Marçal affil: https://ror.org/04pmn0e78 Computer Science Department, University of Alcalá, Alcalá de Henares, Spain su: Sentiment analysis Transformer models Decoding algorithms Natural language processing Computer performance Computational linguistics sug: subj: Sentiment analysis Transformer models Decoding algorithms Natural language processing Computer performance Computational linguistics keyword: Conversational AI Information and Computing Sciences Artificial Intelligence and Image Processing Intent classification Large language models ab: Intent classification and sentiment analysis stand as pivotal tasks in natural language understanding (NLU), with applications ranging from virtual assistants to customer service. The advent of transformer-based models has significantly enhanced the performance of various NLP tasks, with encoder-only architectures gaining prominence for their effectiveness. More recently, there has been a surge in the development of larger and more powerful decoder-only models, traditionally employed for text generation tasks. This paper aims to answer the question of whether the colossal scale of newer decoder-only language models is essential for real-world applications. The investigation involves a performance comparison between these decoder-only models and the well-established encoder-only models specifically in the domains of intent classification and sentiment analysis. The results of our study indicate that, for tasks involving natural language understanding, encoder-only models generally outperform decoder-only models, all while demanding a fraction of the computational resources. This sheds light on the practicality and efficiency of encoder-only architectures in comparison to their decoder-only counterparts in real-world applications, providing valuable insights for the advancement of natural language processing technologies. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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