# LLM-BT-Terms: Back-Translation as a Framework for Terminology Standardization and Dynamic Semantic Embedding

arXiv: https://arxiv.org/abs/2506.08174 · HTML: https://mlsanity.com/paper/2506.08174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [A Survey on Network Embedding](https://mlsanity.com/paper/1711.08752/) (2017)
- [Rethinking embedding coupling in pre-trained language models](https://mlsanity.com/paper/2010.12821/) (2020)
- [Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation](https://mlsanity.com/paper/2505.02836/) (2025)
- [Unsupervised Statistical Machine Translation](https://mlsanity.com/paper/1809.01272/) (2018)
- [LLMs are Also Effective Embedding Models: An In-depth Overview](https://mlsanity.com/paper/2412.12591/) (2024)
- [When Text Embedding Meets Large Language Model: A Comprehensive Survey](https://mlsanity.com/paper/2412.09165/) (2024)
- [A semantic embedding space based on large language models for modelling human beliefs](https://mlsanity.com/paper/2408.07237/) (2024)
- [SLIM-RAFT: A Novel Fine-Tuning Approach to Improve Cross-Linguistic Performance for Mercosur Common Nomenclature](https://mlsanity.com/paper/2408.03936/) (2024)
- [The Paradox of Poetic Intent in Back-Translation: Evaluating the Quality of Large Language Models in Chinese Translation](https://mlsanity.com/paper/2504.16286/) (2025)
- [Improving Language and Modality Transfer in Translation by Character-level Modeling](https://mlsanity.com/paper/2505.24561/) (2025)

## Abstract (arXiv)

The rapid expansion of English technical terminology presents a significant challenge to traditional expert-based standardization, particularly in rapidly developing areas such as artificial intelligence and quantum computing. Manual approaches face difficulties in maintaining consistent multilingual terminology. To address this, we introduce LLM-BT, a back-translation framework powered by large language models (LLMs) designed to automate terminology verification and standardization through cross-lingual semantic alignment. Our key contributions include: (1) term-level consistency validation: by performing English -> intermediate language -> English back-translation, LLM-BT achieves high term consistency across different models (such as GPT-4, DeepSeek, and Grok). Case studies demonstrate over 90 percent of terms are preserved either exactly or semantically; (2) multi-path verification workflow: we develop a novel pipeline described as Retrieve -> Generate -> Verify -> Optimize, which supports both serial paths (e.g., English -> Simplified Chinese -> Traditional Chinese -> English) and parallel paths (e.g., English -> Chinese / Portuguese -> English). BLEU scores and term-level accuracy indicate strong cross-lingual robustness, with BLEU scores exceeding 0.45 and Portuguese term accuracy reaching 100 percent; (3) back-translation as semantic embedding: we reinterpret back-translation as a form of dynamic semantic embedding that uncovers latent trajectories of meaning. In contrast to static embeddings, LLM-BT offers transparent, path-based embeddings shaped by the evolution of the models. This reframing positions back-translation as an active mechanism for multilingual terminology standardization, fostering collaboration between machines and humans - machines preserve semantic integrity, while humans provide cultural interpretation.