# Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination

arXiv: https://arxiv.org/abs/2606.01276 · HTML: https://mlsanity.com/paper/2606.01276/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 31 May 2026.

## Builds on (most cited first, 14 of 14)

- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4](https://mlsanity.com/paper/2403.02839/) (2024)
- [Benchmarking Machine Translation with Cultural Awareness](https://mlsanity.com/paper/2305.14328/) (2023)
- [Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs](https://mlsanity.com/paper/2506.17080/) (2025)
- [TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement](https://mlsanity.com/paper/2402.16379/) (2024)
- [Efficient Cluster-Based k-Nearest-Neighbor Machine Translation](https://mlsanity.com/paper/2204.06175/) (2022)
- [Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning](https://mlsanity.com/paper/2406.07081/) (2024)
- [Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing](https://mlsanity.com/paper/2310.14855/) (2023)
- [Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning](https://mlsanity.com/paper/2410.02631/) (2024)
- [Encouraging Neural Machine Translation to Satisfy Terminology Constraints](https://mlsanity.com/paper/2106.03730/) (2021)
- [Leveraging Domain Knowledge at Inference Time for LLM Translation: Retrieval versus Generation](https://mlsanity.com/paper/2503.05010/) (2025)
- [CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation](https://mlsanity.com/paper/2505.24456/) (2025)

## Abstract (arXiv)

Large language model (LLM)-based machine translation has advanced cross-cultural communication, yet it still struggles with culture-loaded words (CLWs) in ancient Chinese texts. The challenge extends beyond lexical alignment to deciding when and how culture-dependent knowledge should be explicated for readers lacking relevant background. Literal translation often preserves surface forms while missing underlying concepts, whereas over-explicitation harms conciseness and readability. To address this problem, we formulate CLW translation as a selective explicitation task and propose MACAT, a Multi-Agent Culture-Aware Translation framework that dynamically identifies culturally salient phrases and injects concise explanatory knowledge when necessary. MACAT further incorporates a quality-aware reranking module for candidate selection and a multi-round evaluation agent that assesses translations across terminological precision, readability, fidelity, cultural preservation, and cultural explicitation. Experiments on traditional Chinese medicine (TCM) classics and the textitAnalects show that, under a unified GPT-5.4 evaluation setting, MACAT consistently outperforms both the backbone model and general-purpose MT baselines on 100 TCM documents and a 20-chapter subset of the textitAnalects.