Comparing Large Language Model-Based Prompt Engineering Strategies with Feature Engineering Strategies for Complex Word Identification

Prompt engineering has proven effective across various tasks, minimizing reliance on extensive training data. However, its potential for complex word identification (CWI), a key step in lexical simplification, remains unexplored. This study evaluates the effectiveness of prompt engineering for CWI using open-source large language models (LLMs) and compared a new feature engineering-based that integrates diverse features into neural network classifiers. Experimental results show LLMs’ have strong language understanding and generation capabilities, yet feature engineering-based strategy has advantage for such specific classification tasks. Finally, we provide recommendations on how to improving designs of LLMs’ prompts in order for such classification tasks.