M ed COD : Enhancing E nglish-to- S panish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework (2025)

Abstract: We present MedCOD (Medical Chain-of-Dictionary), a hybrid framework designed to improve English-to-Spanish medical translation by integrating domain-specific structured knowledge into large language models (LLMs). MedCOD integrates domain-specific knowledge from both the Unified Medical Language System (UMLS) and the LLM-as-Knowledge-Base (LLM-KB) paradigm to enhance structured prompting and fine-tuning. We constructed a parallel corpus of 2,999 English-Spanish MedlinePlus articles and a 100-sentence test set annotated with structured medical contexts. Four open-source LLMs (Phi-4, Qwen2.5-14B, Qwen2.5-7B, and LLaMA-3.1-8B) were evaluated using structured prompts that incorporated multilingual variants, medical synonyms, and UMLS-derived definitions, combined with LoRA-based fine-tuning. Experimental results demonstrate that MedCOD significantly improves translation quality across all models. For example, Phi-4 with MedCOD and fine-tuning achieved BLEU 44.23, chrF++ 28.91, and COMET 0.863, surpassing strong baseline models like GPT-4o and GPT-4o-mini. Ablation studies confirm that both MedCOD prompting and model adaptation independently contribute to performance gains, with their combination yielding the highest improvements. These findings highlight the potential of structured knowledge integration to enhance LLMs for medical translation tasks.

## introduction electronic health records (ehrs) have become an integral part of modern healthcare, serving as a critical medium for enhancing patient engagement and facilitating better communication between healthcare providers and patients @xcite @xcite . recognizing the value of ehrs, the centers for medicare & medi-* co-corresponding authors caid services (cms) incentive programs have promoted the meaningful use of ehrs, empowering patients to access and manage their health information electronically. however, the full benefits of ehr accessibility are not uniformly realized, particularly among patients with limited english proficiency @xcite @xcite . as of 2021-2022, hispanics account for approximately 19% of the u.s. population (around 63 million people) @xcite . individuals with limited english proficiency (lep) represent about 8% of the total u.s. population, and hispanics make up 62% of the lep group @xcite . this translates to roughly 31-32% of hispanics having limited english skills. language barriers have a welldocumented impact on health care access and comprehension. nearly half of lep adults report encountering challenges in the past three years, such as difficulty filling out medical forms (34%), communicating with health professionals (33%), understanding physician instructions (30%), or following prescription guidelines (27%) @xcite . research further shows that lep is associated with reduced medication adherence and poorer health outcomes, particularly among hispanic patients with chronic conditions like asthma @xcite . conversely, when patients are matched with language-concordant physicians, studies report significant improvements in satisfaction and chronic disease management outcomes @xcite . to address these challenges, machine translation (mt) technologies have been explored for medical communication. traditional statistical mt (smt) and neural mt (nmt) systems @xcite demonstrate promise but often struggle with domain-specific terminology, abbreviations, and context preservation. general-purpose mt systems such as google translate and bing english sentence 2 fever is the classic symptom and may persist for days before any other symptoms appear. ## main results due to space limitations, our ablation study mainly revolves around espacmedlineplus. ## discussion overall effectiveness of medcod our study demonstrates that the integration of domainspecific structured knowledge via the medcod framework substantially enhances the medical translation capabilities of open-source llms. as shown in figure 3 and while the three evaluation metrics generally reflect similar performance trends, they emphasize different aspects of translation quality. sacrebleu and chrf++ measure surface-level similarity, capturing lexical and structural overlap between prediction and reference. in contrast, comet prioritizes semantic adequacy and fluency, aligning more closely with human assessments of meaning. this distinction explains occasional discrepancies between scores and underscores the importance of combining form-and meaning-based metrics to assess clinical translation quality comprehensively. ## conclusion medcod provides a scalable and effective framework for enhancing biomedical translation by leveraging structured domain knowledge and targeted fine-tuning. our results show that open-source llms, when equipped with rich medical context via umls and llm-kb, can rival and even outperform proprietary systems like gpt-4o in clinical translation accuracy. by combining prompting and lightweight adaptation, medcod offers a practical pathway to improve cross-lingual health communication for underrepresented populations.