  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.
