Claude Language Drift Notes

Applicable role: Developer Last updated: 2026-07-09

This article explains the occasional "language drift" phenomenon that occurs when using Claude models, its causes, and how to reduce such output.

Symptoms

When using official Claude models, or calling Claude models through a platform, the following situations occasionally occur:

You ask for answers in Chinese, but the model suddenly mixes in Korean, Japanese, or English partway through
The first half is in Chinese, and the second half suddenly switches to Korean, Japanese, or another language
Language switching is more likely to occur during code explanation, log analysis, long-context conversations, and multilingual material processing
The user did not actively request a language switch, but the model's output language changed

This is not the platform mixing in Korean, Japanese, or other language content in the middle; rather, it is an occasional "language drift" phenomenon inherent to the official Claude models themselves.

Large language models like Claude are trained on large volumes of multilingual data. When generating content, the model does not first fix a single language and use it throughout; instead, it continuously predicts the next most likely Token based on the current context. When the context contains code, abbreviations, foreign-language terms, logs, or multilingual materials, or when there is a degree of randomness in sampling, the model may slip from a Chinese context into English, Japanese, Korean, or other language spaces.

The platform is primarily responsible for unified model access, routing, forwarding, billing, and stability assurance. It does not actively change Chinese content into Korean or Japanese, nor does it insert other-language content into the model's replies. The platform keeps the model's returned content as-is, to avoid introducing new uncertainty by arbitrarily modifying the model's output.

Why Language Drift Happens

1. The model's training data is itself a mix of multiple languages

Large models such as Claude, GPT, and Gemini are exposed to large volumes of multilingual corpora, including Chinese, English, Japanese, Korean, code, web pages, papers, forums, documentation, and more. These languages are not completely isolated; they coexist within the same model generation space.

When generating, the model predicts the probability distribution of the next Token based on context. Therefore, the model is not "only choosing words from a Chinese vocabulary," but rather making selections within a vast multilingual candidate space.

2. Code, abbreviations, and variable names easily break language boundaries

Much language drift occurs in the following scenarios:

Analyzing code or explaining error logs
Processing English variable names or API documentation
Context containing a mix of Chinese and English
User input containing common words such as OK, error, token, model, response
Prompts containing multilingual examples

Such content looks like "code" or "ordinary English words," but in the model's vector representation space, they may be simultaneously related to multiple language regions. If the probabilities of Chinese, English, Japanese, and Korean related Tokens are very close, the model may, due to sampling randomness, select another language direction.

3. The first drifting Token affects subsequent output

Large models generate text by advancing Token by Token. If at some step the model outputs a Japanese or Korean Token, then in the next step the model incorporates that Token into the context as well, further raising the probability of continuing to output that language.

It is as if the model reaches a language fork during generation: it should have continued along the Chinese path, but a certain Token causes it to slip onto the Japanese or Korean path. Once drift begins, the following sentences may continue to be generated in that direction until a new semantic turning point is reached, at which point it may return to Chinese.

4. Sampling randomness amplifies this phenomenon

The model does not always select only the highest-probability word; it makes selections among high-probability candidates according to its sampling strategy.

The lower the temperature, the more stable and conservative the output
The higher the temperature, the more divergent the output, and the more likely language drift becomes
Long contexts, multilingual contexts, and code contexts further increase the probability of drift

How to Reduce Language Drift

1. Use /config to set the language to Simplified Chinese

If you are using Claude Code or a Claude client that supports configuration, we recommend setting the language to Simplified Chinese via /config. This can, to some extent, help the model establish a Chinese output tendency and reduce the probability of switching to another language partway through.

Note that setting the language via /config can mitigate the problem but may not eliminate it 100%, because language drift can still be affected by context, code content, and sampling strategy.

2. Explicitly require fixed Chinese output in the Prompt

We recommend adding explicit language constraints to the System Prompt or user Prompt:

你必须始终使用简体中文回答。除非用户明确要求翻译或保留原文，
否则不要输出英文、日文、韩文或其他语言。即使输入中包含代码、
英文术语、日志、报错信息，也必须使用中文解释。

A stronger-constraint version:

所有回答必须使用简体中文。代码、命令、变量名、函数名、错误信息
可以保留原文，但解释、分析、总结、步骤说明必须全部使用中文。
不要在代码块之外输出英文、日文、韩文或其他语言。
如果上下文中出现外语内容，也必须翻译或解释为中文。

3. Lower the temperature

For API calls, we recommend setting the temperature lower, for example:

{
  "temperature": 0.2
}

For scenarios that require stable output—such as customer service, knowledge-base Q&A, code explanation, and product descriptions—we do not recommend setting the temperature too high.

4. Wrap code and logs in code blocks whenever possible

If the input contains code, logs, errors, JSON, YAML, Shell commands, and similar content, we recommend placing them uniformly in code blocks:

请用中文解释下面的错误原因：
 
```bash
Error: connection timeout when requesting model endpoint
```

This helps the model distinguish which content is source material and which content is the main text that needs to be explained in Chinese.

5. Re-anchor the language when drift occurs

If the model has already started outputting another language, you can simply append a sentence:

请重新用简体中文回答上一条内容，不要使用日文、韩文或英文。

If this happens repeatedly, we recommend starting a new conversation and clearly stating the language requirement in the first Prompt.

If the above methods still do not help and language drift occurs frequently, please contact the platform's technical support for assistance in troubleshooting.