Improving LLM Abilities in Idiomatic Translation (2025)

Abstract: Translating idiomatic expressions remains a challenge for large language models (LLMs), as they often produce literal, semantically incorrect translations—for instance, directly converting “break a leg” into a nonsensical phrase in the target language. While external resources like IdiomKB can supply the figurative meaning and thus yield semantically accurate translations, this approach does not preserve the cultural and stylistic nuances that make idioms so distinctive. Our study focuses on idiomatic translations across multiple languages, including Chinese (ZH), Urdu (UR), and Hindi (HI), with clearly defined abbreviations for each. We propose two methods for improving idiomatic translation fidelity: a Semantic Idiom Alignment (SIA) approach that uses pre-trained sentence embeddings to identify target-language idioms, and a Language-Model-based Idiom Alignment (LIA) approach that prompts an LLM to suggest appropriate idiom counterparts. Human evaluations across multiple language pairs show that SIA better preserves idiomatic style. To support this work, we introduce idiom datasets in low-resource languages (Urdu and Hindi). Our results indicate that aligning idioms at the semantic level can improve cross-lingual style preservation and cultural authenticity.

## introduction global communication increasingly relies on machine translation, yet current large language models (llms) often fail to preserve the cultural and emotional nuances inherent in idiomatic expressions. idioms are linguistically and culturally bound, and translating them accurately is crucial for maintaining the original text's authenticity and resonance. while recent work has augmented llms like nllb and gpt with external knowledge bases (e.g., idiomkb) to improve semantic correctness, these approaches do not retain idiomatic style and contextual richness. this gap motivates our central research question: how can we enhance llm-based translation to capture both the semantic content and the culturally rooted idiomatic flair of the source language? we address this by developing two complementary strategies. first, we propose a semantic idiom alignment (sia) method that leverages pre-trained sentence embeddings (e.g., sentence transformers) and cosine similarity measures to identify target-language idioms closely aligned with the source-language meaning. specifically, we embed the english meaning of each idiom across multiple languages, retrieve top-k candidates via cosine similarity, and then prompt gpt4o to select the most culturally and contextually appropriate match. second, we introduce a llm-based idiom alignment (lia) approach, prompting the llm itself to propose suitable idiomatic counterparts directly. to support these methods, we curate expanded idiom datasets, including low-resource urdu and hindi idioms indexed by their english meanings. we evaluate our approaches through both human judgments and model-based assessments, finding that sia, in particular, improves idiomatic fidelity and cultural nuance across multiple language pairs. our contributions are summarized as follows: (1) introducing methods designed specifically for crosslingual idiom preservation, (2) constructing benchmark datasets for urdu and hindi idioms, and (3) demonstrating that enhancing llm-based translations with idiom alignment can significantly improve stylistic and cultural authenticity. this work lays a foundation for more nuanced and culturally aware machine translation models, ultimately enabling richer, more faithful global communication. ## results the evaluations from our testing presented below reveal the performance of different models for translating idiomatic expressions from english to chinese, chinese to english, english to urdu, and hindi to english. the gpt-4o translations, expectantly, outperformed the gpt3.5-turbo translations. regarding the translation model, the gpt-4o evaluations consistently score the translations lower than the gpt4 evaluations; the evaluation done by gpt-4o matched more closely with the human evaluations. using a binary correlation we found that the gpt4o score matched the human evalua-tion score 65% of the time while the gpt4 score only matched 53% of the time. the superior gpt4o model was more critical of the idiom translations than gpt4, making it a more human-like evaluation. as shown in ## potential risks although relatively risk-free, some risks associated with translation can come to fruition if left overlooked. data bias and representation issues within the knowledge base could lead to culturally insensitive or offensive translations. along the same line of reasoning, language is always evolving, which is why it is important that the knowledge base remains up-to-date, and as comprehensive as possible. if it fails to fit such criteria, misunderstandings could arise, which in important contexts, such as legal, medical, or diplomatic communications could create dire situations. ## conclusion in this paper, we presented advancements in translating idiomatic expressions using llms. we evaluated two methods, semantic idiom alignment, and llmbased idiom alignment, using direct translation as a baseline. our findings indicate that the sia method is particularly effective in preserving idiomatic integrity and achieving higher translation fidelity. despite sometimes yielding worse results than other methods, the sia method proved to be an effective and viable option. lia performed well but fell short compared to the sia, while direct translation often missed idiomatic nuances. human evaluations confirmed the effectiveness of the cosine similarity look-up method, emphasizing the need for context-aware translations. we believe our methods to be very generalizable to other languages if there are adequate datasets. our approach is robust as is compatible and remains effective across languages. the impact of this technology can be proven significant when used to enhance communication through more accurate and culturally resonant translations of literary and educational materials. by making literary works more accessible, this research can help bridge cultural gaps and promote cross-cultural literacy and education globally. it profoundly impacts literary and educational communities by preserving the original tone and style of literary works, allowing readers worldwide to experience texts as intended. by enhancing llms to maintain the style and tone of messages across languages, we acknowledge the crucial role idioms play in communication and how they can express authors' intent in their work, something that is often lost with direct translation from two languages.