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.
@article{arxiv.2410.04925,
title = {Intent Classification for Bank Chatbots through LLM Fine-Tuning},
author = {Bibiána Lajčinová and Patrik Valábek and Michal Spišiak},
journal= {arXiv preprint arXiv:2410.04925},
year = {2024}
}