Lexical Substitution

Lexical substitution, the task of replacing words in a sentence while preserving meaning and context, is a crucial area of natural language processing research. Current work focuses on improving the accuracy and semantic consistency of substitutions using various techniques, including those based on large language models (LLMs) like GPT and Llama, as well as BERT-based contextual embeddings and paraphrasing models. These advancements are driving progress in diverse applications such as data augmentation for hate speech detection and machine translation, improving model robustness and performance. Furthermore, research is exploring the use of lexical substitution for tasks like language proficiency assessment and even text provenance tracing.