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Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The study titled 'Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance' investigates how perturbations in instruction-tuning data can bolster the performance of large language models (LLMs). Previous research highlighted LLMs' sensitivity to minor changes in instruction phrasing, which can hinder their effectiveness. By applying techniques like word shuffling and stop word removal, the authors found that LLMs could not only maintain but sometimes improve their performance on established benchmarks such as MMLU, BBH, and GSM8K. These findings underscore the potential of incorporating perturbed instructions into instruction-tuning processes, enhancing LLMs' robustness against noisy user inputs. This research is pivotal as it opens avenues for developing more reliable AI systems capable of better understanding and responding to varied user instructions.
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