This paper addresses the challenge of Vietnamese Hate Speech Detection (HSD) on social media, where non-standard and noisy text reduces model effectiveness by obscuring meaning. We explore the use of large language models for text restoration and correction (standardization), employing few-shot prompting with Gemini 2.0-Flash and Qwen3 in the context of Vietnamese HSD. The experiments demonstrate that LLM-based standardization, particularly with Gemini 2.0-Flash, improves downstream model performance by increasing the Macro-F1 score from 66.66% on non-standardized data to as high as 68.30%. This result reveals a prominent aspect of LLM-driven standardization as a practical and effective direction for advancing Vietnamese HSD.