TMU-HIT at MLSP 2024: How Well Can GPT-4 Tackle Multilingual Lexical Simplification?

AbstractLexical simplification (LS) is a process of replacing complex words with simpler alternatives to help readers understand sentences seamlessly. This process is divided into two primary subtasks: assessing word complexities and replacing high-complexity words with simpler alternatives. Employing task-specific supervised data to train models is a prevalent strategy for addressing these subtasks. However, such approach cannot be employed for low-resource languages. Therefore, this paper introduces a multilingual LS pipeline system that does not rely on supervised data. Specifically, we have developed systems based on GPT-4 for each subtask. Our systems demonstrated top-class performance on both tasks in many languages. The results indicate that GPT-4 can effectively assess lexical complexity and simplify complex words in a multilingual context with high quality.