In our globalized world, many people naturally mix languages in the same sentence—a phenomenon called code-switching. But can AI tools like ChatGPT, Claude, or Gemini handle these mixed-language prompts effectively? Let’s explore how they perform, where they struggle, and what this means for multilingual communication.

What Are Mixed-Language Prompts?

A mixed-language prompt is a query that blends two or more languages, such as:

“Peux-tu summariser ce texte en English, s’il te plaît ?”

This is common in bilingual or multilingual communities. However, for AI models trained primarily on monolingual data, this can be tricky—they must detect which languages are used, interpret them correctly, and generate a meaningful response.

How LLMs Handle Code-Switching

Modern Large Language Models (LLMs) like GPT-4, mBERT, and XLM-R can often process mixed-language input, especially for common language pairs such as English–French or English–Spanish.

• Strengths:
  • High accuracy for common languages
  • Can translate or respond in the requested output language
  • Reasonably good at detecting context
• Weaknesses:
  • May default to English if unsure
  • Struggles with low-resource languages or informal slang
  • Can misinterpret idioms or tone

A 2024 ACL study found that even strong multilingual LLMs lose accuracy when code-switching, especially in reasoning or domain-specific tasks, unless fine-tuned with mixed-language data (ACL Anthology).

Why It Matters

For tasks like multilingual chatbots, translation, or education, misinterpreting mixed-language input can create confusion or bias. This is why human review and linguistic expertise remain essential for high-stakes or professional communication.

LLMs can handle mixed-language prompts reasonably well for high-resource language pairs, but they remain less reliable with rare languages, slang, or culturally specific expressions. Fine-tuning with code-switching examples and human linguistic oversight are key to making AI truly multilingual.

🔗 Learn more:
→ Code-Switching Red-Teaming Study
→ Hugging Face Multilingual Models

Now that we’ve seen how AI handles mixed-language input, how can we make our prompts clearer and more effective in any language?
Our next article shares practical tips for writing better prompts in other languages so you can get more accurate, culturally aware, and useful AI responses.

👉 Read next: How to Write Better Prompts in Other Languages