ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler

arXiv:2403.17536v1 Announce Type: new
Abstract: State-of-the-art intent classification (IC) and slot filling (SF) methods often rely on data-intensive deep learning models, limiting their practicality for industry applications. Large language models on the other hand, particularly instruction-tuned models (Instruct-LLMs), exhibit remarkable zero-shot performance across various natural language tasks. This study evaluates Instruct-LLMs on popular benchmark datasets for IC and SF, emphasizing their capacity to learn from fewer examples. We introduce ILLUMINER, an approach framing IC and SF as language generation tasks …

abstract applications art arxiv classification classifier cs.cl data deep learning few-shot industry instruction-tuned language language models large language large language models llms natural natural language performance state tasks type zero-shot