Fine-Tuning Medium-Scale LLMs for Joint Intent Classification and Slot Filling: A Data-Efficient and Cost-Effective Solution for SMEs

Abstract

Dialogue Systems (DS) are increasingly in demand for automating tasks through natural language interactions. However, the core techniques for user comprehension in DS depend heavily on large amounts of labeled data, limiting their applicability in data-scarce environments common to many companies. This paper identifies best practices for data-efficient development and cost-effective deployment of DS in real-world application scenarios. We evaluate whether fine-tuning a medium-sized Large Language Model (LLM) for joint Intent Classification (IC) and Slot Filling (SF), with moderate hardware resource requirements still affordable by SMEs, can achieve competitive performance using less data compared to current state-of-the-art models. Experiments on the Spanish and English portions of the MASSIVE corpus demonstrate that the Llama-3-8B-Instruct model fine-tuned with only 10% of the data outperforms the JointBERT architecture and GPT-4o in a zero-shot prompting setup in monolingual settings. In cross-lingual scenarios, Llama-3-8B-Instruct drastically outperforms multilingual JointBERT demonstrating a vastly superior performance when fine-tuned in a language and evaluated in the other.