Intent Classification for Bank Chatbots through LLM Fine-Tuning

Bibiána Lajčinová; Patrik Valábek; Michal Spišiak

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arXiv · 2410.04925

Intent Classification for Bank Chatbots through LLM Fine-Tuning

Bibiána Lajčinová·Patrik Valábek·Michal Spišiak

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Abstract

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.

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BibTeX RIS

Bibiána Lajčinová, Patrik Valábek, Michal Spišiak. 2024-10-07. Intent Classification for Bank Chatbots through LLM Fine-Tuning. https://arxiv.org/abs/2410.04925

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