🤖 Why AI Performs Worse in Non-English Languages
The gap in LLM performance quality is related to technical factors rather than linguistics: training data distribution, tokenizer inefficiency, and a lack of instruction-tuning. Poor tokenization makes using models in other languages 3–4 times more expensive and reduces the effective context window, while retrieval accuracy can drop by up to 29%.
🌍 The problem requires a transition from universal models to regional solutions or a reallocation of resources toward training tokenizers and instruction fine-tuning for underrepresented languages.
👤 For better results, use English prompts for logic while requesting the response in the target language. Keep in mind that API costs and available context volume for non-English texts will be higher and smaller, respectively.