Translation Prompt

Translation prompting investigates how to effectively guide large language models (LLMs) to perform tasks involving multiple languages, focusing on optimizing prompt design for improved accuracy and efficiency across diverse linguistic contexts. Current research explores various prompting strategies, including multilingual prompts, few-shot learning, and the use of English as a pivot language, evaluating their effectiveness across different LLM architectures and benchmark datasets. This research is crucial for advancing multilingual natural language processing, enabling broader access to LLM capabilities for low-resource languages and improving the performance of cross-lingual applications such as machine translation and cross-lingual information retrieval.