Intent Classification for Bank Chatbots through LLM Fine-Tuning
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866909338527858688 |
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| author | Lajčinová, Bibiána Valábek, Patrik Spišiak, Michal |
| author_facet | Lajčinová, Bibiána Valábek, Patrik Spišiak, Michal |
| contents | This study evaluates the application of large language models (LLMs) for intent classification within a chatbot with predetermined responses designed for banking industry websites. Specifically, the research examines the effectiveness of fine-tuning SlovakBERT compared to employing multilingual generative models, such as Llama 8b instruct and Gemma 7b instruct, in both their pre-trained and fine-tuned versions. The findings indicate that SlovakBERT outperforms the other models in terms of in-scope accuracy and out-of-scope false positive rate, establishing it as the benchmark for this application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04925 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intent Classification for Bank Chatbots through LLM Fine-Tuning Lajčinová, Bibiána Valábek, Patrik Spišiak, Michal Computation and Language I.2.7 This study evaluates the application of large language models (LLMs) for intent classification within a chatbot with predetermined responses designed for banking industry websites. Specifically, the research examines the effectiveness of fine-tuning SlovakBERT compared to employing multilingual generative models, such as Llama 8b instruct and Gemma 7b instruct, in both their pre-trained and fine-tuned versions. The findings indicate that SlovakBERT outperforms the other models in terms of in-scope accuracy and out-of-scope false positive rate, establishing it as the benchmark for this application. |
| title | Intent Classification for Bank Chatbots through LLM Fine-Tuning |
| topic | Computation and Language I.2.7 |
| url | https://arxiv.org/abs/2410.04925 |