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Meaning-Making Process and Error Dynamics in ChatGPT-Mediated Translation

Summary

The study looks at how translation trainees handle LLM output when post-editing a specialised text, using German–Spanish, a pair largely missing from MTPE research and benchmarks. Benchmarks such as WMT APE, LangMark and DivEMT are dominated by English-centred pairs. Twenty students post-edited a ChatGPT-4.5 translation of a short German economic article on inflation into Spanish. The authors annotated the resulting errors along two dimensions: where each error came from (the model or the student's own revision) and which linguistic category it belonged to.

Half of the problems trace back to the model. A sizeable share, however, were created by students rewriting segments that were already fine. Terminology was by far the most error-prone area. The authors argue that ChatGPT's smooth, natural-sounding Spanish lowers trainees' vigilance toward meaning and domain inaccuracies, while also inviting needless reformulation.

Comparing these results with an earlier DeepL-based study, they propose that trainees check adequacy first and polish form afterwards. They also call for training in LLM literacy, explicit decision thresholds for editing, and genre-aware use of glossaries.

Key points

  • Data: 132 annotated instances from a 127-word source text post-edited by 20 students. Only 12 students produced annotated errors, and 8 had none.
  • By origin: 50.8% of instances are ChatGPT-related, 21.2% were introduced by students through over-editing, and 28.0% are acceptable preferential changes.
  • By category: terminology is the riskiest at 34.1%, followed by tense/aspect at 15.2% and style at 13.6%.
  • Recommended practice: a two-pass QA protocol covering terminology, tense/aspect and meaning transfer first, then orthotypography, punctuation and style.
  • Limitations: a single text, a single LLM (ChatGPT-4.5), and no process data. Future work should add keystroke logs or screen recordings and test other LLM and NMT systems.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Machine translation

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{mihalache2026meaning,
  title = {{Meaning-Making Process and Error Dynamics in ChatGPT-Mediated Translation}},
  author = {Mihalache, Iulia and Salinas, María-José Varela},
  booktitle = {Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)},
  year = {2026},
  url = {https://aclanthology.org/2026.eamt-1.40/}
}

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