ILLUMINER：作为少样本意图分类器和槽位填充器的指令微调大型语言模型
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler
AI总结：
本研究提出ILLUMINER方法，将意图分类和槽位填充构建为Instruct-LLMs的语言生成任务，使用FLAN-T5 11B模型在少样本场景下显著超越现有方法和GPT3.5，并证明参数高效微调仅需不到6%训练数据即可媲美全权重微调。
摘要：
最先进的意图分类（IC）和槽位填充（SF）方法通常依赖于数据密集型的深度学习模型，这限制了它们在工业应用中的实用性。另一方面，大型语言模型，特别是指令微调模型（Instruct-LLMs），在各种自然语言任务中展现出了卓越的零样本性能。本研究在流行的IC和SF基准数据集上评估了Instruct-LLMs，强调了它们从较少样本中学习的能力。我们引入了ILLUMINER，该方法将IC和SF构建为Instruct-LLMs的语言生成任务，与先前工作相比具有更高效的SF提示方法。与多个基线的全面比较表明，我们使用FLAN-T5 11B模型的方法优于最先进的联合IC+SF方法以及GPT3.5（175B）的上下文学习，特别是在槽位填充上高出11.1--32.2个百分点。此外，我们深入的消融研究表明，参数高效微调只需不到6%的训练数据即可产生与传统全权重微调相当的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
