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Large language models LLMs have revolutionized natural language processing by demonstrating impressive abilities to perform a wide range of tasks, including machine translation MT. However, the quality and domain of the in-context examples used to prompt these models can significantly impact their performance for specific tasks. In this talk, I will discuss two recent papers that propose to optimize in-context examples and leverage bilingual dictionaries to enhance the quality and controllability of MT with LLMs. First, I will explore the impact of in-context examples on the translation quality of LLMs and highlight the challenges of selecting good examples in both in-domain and out-of-domain settings. Then, I will discuss how we can leverage bilingual dictionaries to provide fine-grained phrase-level control hints in the prompts of LLMs.
Marjan Ghazvininejad is a senior research scientist at Facebook AI Research. She received her Ph.D. at the University of Southern California on neural creative language generation. Her research interests include text representation, language generation, and machine translation. Her recent research has focused on how to optimize the use of large language models in various applications.