  Generative large language models (LLMs) have demonstrated exceptional
proficiency in various natural language processing (NLP) tasks, including
machine translation, question answering, text summarization, and natural
language understanding.
  To further enhance the performance of LLMs in machine translation, we
conducted an investigation into two popular prompting methods and their
combination, focusing on cross-language combinations of Persian, English, and
Russian. We employed n-shot feeding and tailored prompting frameworks. Our
findings indicate that multilingual LLMs like PaLM exhibit human-like machine
translation outputs, enabling superior fine-tuning of desired translation
nuances in accordance with style guidelines and linguistic considerations.
These models also excel in processing and applying prompts. However, the choice
of language model, machine translation task, and the specific source and target
languages necessitate certain considerations when adopting prompting frameworks
and utilizing n-shot in-context learning.
  Furthermore, we identified errors and limitations inherent in popular LLMs as
machine translation tools and categorized them based on various linguistic
metrics. This typology of errors provides valuable insights for utilizing LLMs
effectively and offers methods for designing prompts for in-context learning.
Our report aims to contribute to the advancement of machine translation with
LLMs by improving both the accuracy and reliability of evaluation metrics.
