ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent
  Classifier and Slot Filler
Paramita Mirza Viju Sudhi Soumya Ranjan Sahoo Sinchana Ramakanth Bhat
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 for
Instruct-LLMs, with a more efficient SF-prompting method compared to prior
work. A comprehensive comparison with multiple baselines shows that our
approach, using the FLAN-T5 11B model, outperforms the state-of-the-art joint
IC+SF method and in-context learning with GPT3.5 (175B), particularly in slot
filling by 11.1--32.2 percentage points. Additionally, our in-depth ablation
study demonstrates that parameter-efficient fine-tuning requires less than 6%
of training data to yield comparable performance with traditional full-weight
fine-tuning.