Discover the key advantage of using pre-trained language models over traditional NLP techniques. Learn how transfer learning enables faster development and improves performance in various NLP tasks.
Table of Contents
Question
The main advantage of using pre-trained language models compared to traditional NLP techniques is
A. Faster development due to transfer learning
B. Lower computational requirements
C. Greater interpretability
D. Ability to handle out-of-distribution inputs effectively
Answer
A. Faster development due to transfer learning
Explanation
The main advantage of using pre-trained language models compared to traditional NLP techniques is faster development due to transfer learning.
Transfer learning is a technique where knowledge gained from solving one problem is applied to a different but related problem. In the context of NLP, pre-trained language models like BERT, GPT, and XLNet are trained on massive amounts of text data, allowing them to learn general language representations and patterns.
These pre-trained models can then be fine-tuned for specific downstream tasks such as sentiment analysis, named entity recognition, or question answering with relatively small amounts of task-specific data. This process is significantly faster than training a model from scratch for each individual task.
Traditional NLP techniques, on the other hand, often require extensive feature engineering and task-specific model training, which can be time-consuming and resource-intensive. Pre-trained language models eliminate the need for manual feature engineering and enable developers to leverage the knowledge learned from large-scale unsupervised training.
By using pre-trained language models, developers can achieve state-of-the-art performance on various NLP tasks with minimal fine-tuning and training time. This accelerates the development process and allows for rapid prototyping and deployment of NLP applications.
While pre-trained language models offer computational efficiency and improved performance, they may not necessarily provide greater interpretability or handle out-of-distribution inputs effectively without additional techniques or modifications.
In summary, the main advantage of using pre-trained language models is faster development due to transfer learning, which enables developers to leverage the knowledge learned from large-scale unsupervised training and achieve high performance on specific NLP tasks with minimal fine-tuning and training time.
Infosys Certified Applied Generative AI Professional certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Infosys Certified Applied Generative AI Professional exam and earn Infosys Certified Applied Generative AI Professional certification.