# Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing

arXiv: https://arxiv.org/abs/2606.10113 · HTML: https://mlsanity.com/paper/2606.10113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://mlsanity.com/paper/2402.17733/) (2024)
- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation](https://mlsanity.com/paper/1906.12068/) (2019)
- [Do GPTs Produce Less Literal Translations?](https://mlsanity.com/paper/2305.16806/) (2023)
- [Post-editese: an Exacerbated Translationese](https://mlsanity.com/paper/1907.00900/) (2019)
- [How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs](https://mlsanity.com/paper/2410.18697/) (2024)
- [Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis](https://mlsanity.com/paper/2306.02213/) (2023)
- [Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing](https://mlsanity.com/paper/2504.03045/) (2025)

## Abstract (arXiv)

This paper investigates whether LLM translations exhibit identifiable emotional profiles and how post-editing reshapes them toward human-like norms. We compare LLM translations of Margaret Atwood's Oryx and Crake with their post-edited versions and a human translation, using a large-scale corpus of contemporary Italian science-fiction as a baseline. We examine emotion through lexicon-based and multilingual modeling, conducting a fine-grained analysis of emotional variation across systems. We find that MT systems introduce model-specific and statistically significant emotional fingerprints across translations, leading to a limited preservation of an author's voice.