When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness
  Methods for LLMs
Authors: name: Mikhail Seleznyov, name: Mikhail Chaichuk, name: Gleb Ershov, name: Alexander Panchenko, name: Elena Tutubalina, name: Oleg Somov

Large Language Models (LLMs) are highly sensitive to subtle, non-semantic
variations in prompt phrasing and formatting. In this work, we present the
first systematic evaluation of 5 methods for improving prompt robustness within
a unified experimental framework. We benchmark these techniques on 8 models
from Llama, Qwen and Gemma families across 52 tasks from Natural Instructions
dataset. Our evaluation covers robustness methods from both fine-tuned and
in-context learning paradigms, and tests their generalization against multiple
types of distribution shifts. Finally, we extend our analysis to GPT-4.1 and
DeepSeek V3 to assess frontier models' current robustness to format
perturbations. Our findings offer actionable insights into the relative
effectiveness of these robustness methods, enabling practitioners to make
informed decisions when aiming for stable and reliable LLM performance in
real-world applications. Code:
https://github.com/AIRI-Institute/when-punctuation-matters.