Machine Translation (MT) is an essential tool for communication amongst people across different cultures, yet Named Entity (NE) translation remains a major challenge due to its rarity in occurrence and ambiguity. Traditional approaches, like using lexicons or parallel corpora, often fail to generalize to unseen entities, and hence do not perform well. To address this, we create a silver dataset using the Google Translate API and fine-tune the facebook/nllb200-distilled-600M model with LoRA (LowRank Adaptation) to enhance translation accuracy while also maintaining efficient memory use. Evaluated with metrics such as BLEU, COMET, and M-ETA, our results show that fine-tuning a specialized MT model improves NE translation without having to rely on largescale general-purpose models.

In recent studies, the extensive utilization oflarge language models has underscored the importance of robust evaluation methodologiesfor assessing text generation quality and relevance to specific tasks. This has revealeda prevalent issue known as hallucination, anemergent condition in the model where generated text lacks faithfulness to the source anddeviates from the evaluation criteria. In thisstudy, we formally define hallucination and propose a framework for its quantitative detectionin a zero-shot setting, leveraging our definitionand the assumption that model outputs entailtask and sample specific inputs. In detectinghallucinations, our solution achieves an accuracy of 0.78 in a model-aware setting and 0.61in a model-agnostic setting. Notably, our solution maintains computational efficiency, requiring far less computational resources than other SOTA approaches, aligning with the trendtowards lightweight and compressed models.