用微调BERT提升银行客服意图识别准确率
Intent Classification for Bank Chatbots through LLM Fine-Tuning
- 对比微调SlovakBERT与通用大模型的分类效果
- SlovakBERT在正确识别率和误报率上均更优
- 适合金融领域垂直场景的对话系统优化
本研究评估了大型语言模型（LLMs）在银行网站预设回复聊天机器人中的意图分类应用。具体比较了微调SlovakBERT与多语言生成模型（如Llama 8b instruct和Gemma 7b instruct）在预训练及微调版本下的表现。结果表明，SlovakBERT在范围内的准确率和范围外的误报率方面均优于其他模型，成为该应用场景的基准。
原文摘要 · Abstract (English)
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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