Recently, Instruction fine-tuning has risen to prominence as a potential method for enhancing the zero-shot capabilities of Large Language Models (LLMs) on novel tasks. This technique has shown an exceptional ability to boost the performance of moderately sized LLMs, sometimes even reaching performance levels comparable to those of much larger model variants. The focus is on the robustness of instruction-tuned LLMs to seen and unseen tasks. We conducted an exploration of six models including Alpaca, Vicuna, WizardLM, and Traditional Task-oriented Models(Flan-T5-XL/XXL, T0++) using real-world relation extraction datasets as case studies. We carried out a comprehensive evaluation of these instruction-following LLMs which have been tuned based on open-domain instructions and task-oriented instructions. The main discussion is their performance and robustness towards instructions. We have observed that in most cases, the model's performance in dealing with unfamiliar instructions tends to worsen significantly, and the robustness of the model for RE instructions deteriorates compared to QA. Further, we discovered that up until a certain parameter size threshold (3B), the performance of the FLAN-T5 model improves as the parameter count increases. The robustness of different scales of FLAN-T5 models to RE instruction is worse than the robustness to QA instruction.

翻译：近年来，指令微调作为一种增强大型语言模型（LLMs）在新任务上零样本能力的潜在方法而备受关注。该技术表现出卓越的能力，能够提升中等规模LLMs的性能，有时甚至能达到与更大规模模型相当的水平。本文聚焦于指令微调后的LLMs在已知与未知任务上的鲁棒性。我们以真实关系抽取数据集为案例，对包括Alpaca、Vicuna、WizardLM以及传统任务导向模型（Flan-T5-XL/XXL、T0++）在内的六个模型进行了探索。我们全面评估了这些基于开放域指令和任务导向指令微调后的指令遵循型LLMs，主要讨论了它们对指令的性能表现与鲁棒性。我们观察到，在大多数情况下，模型处理不熟悉指令时性能显著恶化，且相较于问答（QA）任务，模型对关系抽取（RE）指令的鲁棒性更差。此外，我们发现在达到特定参数规模阈值（3B）之前，FLAN-T5模型的性能随参数数量增加而提升。不同规模的FLAN-T5模型对RE指令的鲁棒性均弱于对QA指令的鲁棒性。