Vocabulary Trimming

Vocabulary trimming, the process of reducing the size of a language model's vocabulary, aims to improve efficiency and resource utilization without significantly sacrificing performance. Current research focuses on applying this technique to various model architectures, including transformer-based language models and latent Dirichlet allocation (LDA) models, often in conjunction with other compression methods like knowledge distillation. Findings regarding the effectiveness of vocabulary trimming are mixed, with some studies showing substantial benefits in model size reduction with minimal performance loss, while others demonstrate performance degradation, highlighting the need for careful consideration of trimming strategies and evaluation metrics. This research area is significant for deploying large language models in resource-constrained environments and for improving the efficiency of natural language processing tasks.