Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing
Antonio Castaldo, Sheila Castilho, Joss Moorkens, Johanna Monti
Abstract
Post-editing machine translation (MT) for creative texts, such as literature, requires balancing efficiency with the preservation of creativity and style. While neural MT systems struggle with these challenges, large language models (LLMs) offer improved capabilities for context-aware and creative translation. This study evaluates the feasibility of post-editing literary translations generated by LLMs. Using a custom research tool, we collaborated with professional literary translators to analyze editing time, quality, and creativity. Our results indicate that post-editing (PE) LLM-generated translations significantly reduce editing time compared to human translation while maintaining a similar level of creativity. The minimal difference in creativity between PE and MT, combined with substantial productivity gains, suggests that LLMs may effectively support literary translators.- Anthology ID:
- 2025.mtsummit-1.40
- Volume:
- Proceedings of Machine Translation Summit XX: Volume 1
- Month:
- June
- Year:
- 2025
- Address:
- Geneva, Switzerland
- Editors:
- Pierrette Bouillon, Johanna Gerlach, Sabrina Girletti, Lise Volkart, Raphael Rubino, Rico Sennrich, Ana C. Farinha, Marco Gaido, Joke Daems, Dorothy Kenny, Helena Moniz, Sara Szoc
- Venue:
- MTSummit
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 506–515
- Language:
- URL:
- https://preview.aclanthology.org/mtsummit-25-ingestion/2025.mtsummit-1.40/
- DOI:
- Cite (ACL):
- Antonio Castaldo, Sheila Castilho, Joss Moorkens, and Johanna Monti. 2025. Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing. In Proceedings of Machine Translation Summit XX: Volume 1, pages 506–515, Geneva, Switzerland. European Association for Machine Translation.
- Cite (Informal):
- Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing (Castaldo et al., MTSummit 2025)
- PDF:
- https://preview.aclanthology.org/mtsummit-25-ingestion/2025.mtsummit-1.40.pdf