# Towards Style Alignment in Cross-Cultural Translation

arXiv: https://arxiv.org/abs/2507.00216 · HTML: https://mlsanity.com/paper/2507.00216/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 58 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 25 of 25)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer](https://mlsanity.com/paper/1803.06535/) (2018)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [The unreasonable effectiveness of few-shot learning for machine translation](https://mlsanity.com/paper/2302.01398/) (2023)
- [Benchmarking Machine Translation with Cultural Awareness](https://mlsanity.com/paper/2305.14328/) (2023)
- [XFORMAL: A Benchmark for Multilingual Formality Style Transfer](https://mlsanity.com/paper/2104.04108/) (2021)
- [TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling](https://mlsanity.com/paper/2010.03802/) (2020)
- [Multi-Task Neural Models for Translating Between Styles Within and Across Languages](https://mlsanity.com/paper/1806.04357/) (2018)
- [CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality](https://mlsanity.com/paper/2205.04022/) (2022)
- [Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models](https://mlsanity.com/paper/2308.13961/) (2023)
- [Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs](https://mlsanity.com/paper/2410.14057/) (2024)
- [Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings](https://mlsanity.com/paper/2110.07385/) (2021)
- [TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages](https://mlsanity.com/paper/2211.16496/) (2022)
- [FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation](https://mlsanity.com/paper/2210.00193/) (2022)
- [Controlling Translation Formality Using Pre-trained Multilingual Language Models](https://mlsanity.com/paper/2205.06644/) (2022)
- [SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis](https://mlsanity.com/paper/2210.01108/) (2022)
- [Multilingual Text Style Transfer: Datasets & Models for Indian Languages](https://mlsanity.com/paper/2405.20805/) (2024)
- [Retrieval-Augmented Machine Translation with Unstructured Knowledge](https://mlsanity.com/paper/2412.04342/) (2024)
- [Towards Universality in Multilingual Text Rewriting](https://mlsanity.com/paper/2107.14749/) (2021)

## Built on by (most cited first, 4 of 4)

- [Culturally-Aware Conversations: A Framework & Benchmark for LLMs](https://mlsanity.com/paper/2510.11563/) (2025)
- [VideoNorms: Benchmarking Cultural Awareness of Video Language Models](https://mlsanity.com/paper/2510.08543/) (2025)
- [Semantic Label Drift in Cross-Cultural Translation](https://mlsanity.com/paper/2510.25967/) (2025)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)

## Abstract (arXiv)

Successful communication depends on the speaker's intended style (i.e., what the speaker is trying to convey) aligning with the listener's interpreted style (i.e., what the listener perceives). However, cultural differences often lead to misalignment between the two; for example, politeness is often lost in translation. We characterize the ways that LLMs fail to translate style - biasing translations towards neutrality and performing worse in non-Western languages. We mitigate these failures with RASTA (Retrieval-Augmented STylistic Alignment), a method that leverages learned stylistic concepts to encourage LLM translation to appropriately convey cultural communication norms and align style.