Resilience of Large Language Models for Noisy Instructions

TL;DR

This paper examines how large language models respond to various types of noisy instructions, revealing performance degradation and exploring a correction strategy to improve resilience.

Contribution

It provides a comprehensive analysis of LLM resilience to common instruction errors and evaluates a noise correction method to enhance robustness.

Findings

LLMs' performance drops significantly with noisy instructions

Some models show partial resistance to certain error types

Correcting noisy instructions is challenging, especially for open-source models

Abstract

As the rapidly advancing domain of natural language processing (NLP), large language models (LLMs) have emerged as powerful tools for interpreting human commands and generating text across various tasks. Nonetheless, the resilience of LLMs to handle text containing inherent errors, stemming from human interactions and collaborative systems, has not been thoroughly explored. Our study investigates the resilience of LLMs against five common types of disruptions including 1) ASR (Automatic Speech Recognition) errors, 2) OCR (Optical Character Recognition) errors, 3) grammatical mistakes, 4) typographical errors, and 5) distractive content. We aim to investigate how these models react by deliberately embedding these errors into instructions. Our findings reveal that while some LLMs show a degree of resistance to certain types of noise, their overall performance significantly suffers. This emphasizes the importance of further investigation into enhancing model resilience. In response to the observed decline in performance, our study also evaluates a "re-pass" strategy, designed to purify the instructions of noise before the LLMs process them. Our analysis indicates that correcting noisy instructions, particularly for open-source LLMs, presents significant challenges.