New Survey Explores How Large Language Models Transform NLP Tasks
A comprehensive new survey examines the impact of large pre-trained transformer models like BERT on natural language processing. The paper highlights methods like fine-tuning and prompting while addressing current limitations.
A new survey explores how large, pre-trained transformer-based language models like BERT fundamentally change the field of Natural Language Processing (NLP). The researchers review recent studies that leverage these massive models to tackle a wide variety of complex linguistic tasks.
The paper categorizes the practical application of these models into three main approaches: pre-training followed by fine-tuning, prompting, and direct text generation. Additionally, the authors examine how developers use pre-trained language models to artificially generate data for training augmentation and other specialized purposes.
Alongside highlighting these technological achievements, the survey explicitly addresses the inherent limitations of current large language models. The authors conclude by suggesting clear, actionable directions for future research to overcome these hurdles and further advance the capabilities of NLP systems.