The problem: correct, but it still reads like a translation
Most of us have seen this. A ChatGPT translation is accurate, and you still know it was translated. The sentence structure follows the original, and a few phrases are ones no native speaker would say. Linguists call this translationese. You can’t point to a single wrong word, yet the whole paragraph feels slightly off, like a friend reading a speech in a language they learned from a textbook.
A Reddit user in r/ChatGPTPromptGenius shared research that puts numbers on it. An ACL 2025 paper called Lost in Literalism had human annotators check document translations (English to Chinese, German to English) from several models, GPT-4 Turbo among them. More than 40% of GPT-4’s translations had noticeable translationese. That’s a lot of text that is right on paper and still wrong in the ear of the person reading it.
What the researchers tried
They tested two fixes:
- 🧭 Instruct up front. Tell the model to avoid unnatural, literal translation. This didn’t help, and GPT-4 came out slightly worse.
- ✍️ Polish after. Hand the model its own translation and ask it to improve it. The share of translationese dropped from 43% to 25%.
The poster’s guess at why is a good one. While translating, the model follows the source sentence by sentence. It is busy keeping every piece of meaning, so it stays close to the original shape. While polishing, it reads the result as plain text in the target language, and that’s when the stiff parts stand out. Think of how you catch typos in your own email only after you hit send and read it again. A second pass with a different job gives you a different kind of attention.
The three-step chain
Step 1: translate with context
Translate the text below into {{language}}. The reader is {{reader}}. Keep every fact, number and name exactly as it is.
{{text}}
Be specific about the reader. “Busy small business owners in Germany” gives the model far more to work with than “general audience”, and it shapes formality, word choice and sentence length from the first draft.
Step 2: polish as a native reader
Now read your translation as a native speaker of that language who has never seen the original. Rewrite every sentence that sounds translated: word order copied from the source, phrases nobody would actually say, or a tone that’s wrong for this reader. Don’t add or remove any meaning. Then list each change and why you made it, in English.
Step 3: back-translate, for languages you can’t read
Translate your final version back into English, sentence by sentence, as literally as you can, so I can check that no meaning changed in the rewrite.
If the back-translation says something your original didn’t, ask the model to fix that one sentence and keep the rest. Quote the sentence in your message so it doesn’t rewrite the whole text again and undo the good parts.
Two caveats worth knowing
- The paper tested older models. The gap is probably smaller on current ones, so run a quick test on whatever model you use. Take one paragraph you know well, run it with and without Step 2, and see if you can feel the difference.
- Step 2 can get carried away with idioms. That’s why the prompt makes it list every change. The list is your audit trail, so read it. If you see a change that swaps a plain phrase for a flashy saying, push back and ask for the simpler version.
Use cases
- Localizing landing pages, emails or product descriptions for another market
- Translating support replies where tone matters as much as accuracy
- Checking a translation in a language you can’t read, using the back-translation step
- Turning a technical doc into something a normal reader will finish
Prompt of the Day
If you only keep one piece, keep Step 2. Paste it right after any translation you’ve already got, in any chat. It works as a standalone polish pass, and the change list tells you what moved. That also makes it handy for translations a colleague or another tool made, since the model doesn’t need to have written the first draft to improve it.
One more note
The original poster built AI Toolbox, which saves this as a chain. You type .. in the message box, pick the chain, and fill in the language, the reader and the text once. Each step waits for the previous answer before it sends. It works in ChatGPT, Claude, Gemini and Grok, and the free plan holds 2 chains. You can also just copy and paste the three prompts by hand. It takes an extra minute, and you’ll see exactly what each step does.
Your turn
Run your usual translation through the two-step version and compare it to your old result. Then tell me which language pair you translate most and how much Step 2 changed for it. That’s the data point I’m after.
Telling ChatGPT to “sound natural” doesn’t fix its translations. Asking it to polish the translation it already wrote does
by u/Ok_Negotiation_2587 in ChatGPTPromptGenius