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Instruction tuning has remarkably advanced large language models (LLMs) in understand- ing and responding to diverse human instruc- tions. Despite the success in high-resource lan- guages, its application in lower-resource ones faces challenges due to the imbalanced foun- dational abilities of LLMs across different lan- guages, stemming from the uneven language distribution in their pre-training data. To tackle this issue, we propose pivot language guided generation (PLUG), an approach that utilizes a high-resource language, primarily English, as the pivot to enhance instruction tuning in lower-resource languages. It trains the model to first process instructions in the pivot language, and then produce responses in the target lan- guage. To evaluate our approach, we introduce a benchmark, X-AlpacaEval, of instructions in 4 languages (Chinese, Korean, Italian, and Spanish), each annotated by professional trans- lators. Our approach demonstrates a significant improvement in the instruction-following abili- ties of LLMs by 29% on average, compared to directly responding in the target language alone. Further experiments validate the versatility of our approach by employing alternative pivot languages beyond English to assist languages where LLMs exhibit lower proficiency.