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.

翻译：本研究评估了大型语言模型在银行业网站预设响应聊天机器人中进行意图分类的应用。具体而言，研究比较了微调SlovakBERT模型与采用多语言生成模型（如Llama 8b instruct和Gemma 7b instruct）在预训练版本和微调版本上的性能表现。结果表明，SlovakBERT在领域内分类准确率和领域外误报率方面均优于其他模型，确立了其在该应用场景中的基准地位。