Intent Classification for Bank Chatbots through LLM Fine-Tuning
<|reference_start|>Intent Classification for Bank Chatbots through LLM Fine-Tuning: 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.<|reference_end|>
@article{lajčinová2024intent,
    title={Intent Classification for Bank Chatbots through LLM Fine-Tuning},
    author={Bibi'ana Lajv{c}inov'a, Patrik Val'abek, Michal Spiv{s}iak},
    journal={arXiv preprint arXiv:2410.04925},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04925},
    primaryClass={cs.CL}
}