Evaluating the Robustness to Instructions of Large Language Models

TL;DR

This paper evaluates how instruction-tuned large language models perform and maintain robustness on seen and unseen tasks, especially relation extraction, revealing performance drops with unfamiliar instructions and size-dependent robustness patterns.

Contribution

It provides a comprehensive evaluation of multiple instruction-tuned LLMs on real-world tasks, highlighting robustness issues and size-related performance trends.

Findings

Performance drops on unseen instructions, especially for relation extraction.

Robustness to RE instructions is worse than to QA instructions.

Performance of FLAN-T5 improves with size up to 3B parameters.

Abstract

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.