Fine-Tuning on Noisy Instructions: Effects on Generalization and
  Performance
Instruction-tuning plays a vital role in enhancing the task-solving abilities
of large language models (LLMs), improving their usability in generating
helpful responses on various tasks. However, previous work has demonstrated
that they are sensitive to minor variations in instruction phrasing. In this
paper, we explore whether introducing perturbations in instruction-tuning data
can enhance LLMs' resistance against noisy instructions. We focus on how
instruction-tuning with perturbations, such as removing stop words or shuffling
words, affects LLMs' performance on the original and perturbed versions of
widely-used benchmarks (MMLU, BBH, GSM8K). We further assess learning dynamics
and potential shifts in model behavior. Surprisingly, our results suggest that
instruction-tuning on perturbed instructions can, in some cases, improve
downstream performance. These findings highlight the importance of including
perturbed instructions in instruction-tuning, which can make LLMs more
resilient to noisy user inputs.
68e5275e7e1f41b92bf8e9f4
Ahmed Alajrami
claimed_verified
2025-10-08T08:01:22.730Z
60cf445f43c9305fa34dd87c
/avatars/1b24521a4bcfda7d88fcd8fa592ae159.svg
Ahmed Alajrami
user
aajrami
68e5275e7e1f41b92bf8e9f5
Xingwei Tan
claimed_verified
2025-10-08T08:01:25.137Z
643d0a4d8a55b2bbf4f2a90e
/avatars/9534aaf81cbf12f015c6826b682fdb84.svg
Xingwei Tan
user
XingweiT
68e5275e7e1f41b92bf8e9f6
Nikolaos Aletras
Fine-Tuning on Noisy Instructions: Effects on Generalization and
  Performance
Instruction-tuning plays a vital role in enhancing the task-solving abilities
of large language models (LLMs), improving their usability in generating
helpful responses on various tasks. However, previous work has demonstrated
that they are sensitive to minor variations in instruction phrasing. In this
paper, we explore whether introducing perturbations in instruction-tuning data
can enhance LLMs' resistance against noisy instructions. We focus on how
instruction-tuning with perturbations, such as removing stop words or shuffling
words, affects LLMs' performance on the original and perturbed versions of
widely-used benchmarks (MMLU, BBH, GSM8K). We further assess learning dynamics
and potential shifts in model behavior. Surprisingly, our results suggest that
instruction-tuning on perturbed instructions can, in some cases, improve
downstream performance. These findings highlight the importance of including
perturbed instructions in instruction-tuning, which can make LLMs more
resilient to noisy user inputs.
Introducing perturbations in instruction-tuning data can enhance large language models' resistance to noisy instructions and improve performance on benchmarks.
instruction-tuning large language models LLMs perturbations stop words word shuffling MMLU BBH GSM8K learning dynamics model behavior noisy user inputs