Learn Before
Pre-trained Language Models
A breakthrough in NLP and deep learning, pre-trained language models (PLMs) are trained on large-scale unlabeled data, which allows them to grasp diverse knowledge. These models serve as 'foundation models' that can be adapted to a wide range of downstream tasks, often through fine-tuning on a small amount of supervised data, leading to state-of-the-art results. This approach has fundamentally changed the NLP development paradigm.
0
1
Contributors are:
Who are from:
Tags
Deep Learning (in Machine learning)
Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models Course
Data Science
Computing Sciences
Learn After
Controlled text generation using PLMs
Representative Transformer-based PLMs
Analysis of Language Model Training Strategies
A startup is developing a system to classify medical research abstracts into different fields of study (e.g., cardiology, oncology, neurology). They have a limited dataset of 10,000 labeled abstracts. Which of the following statements best justifies the decision to use a large, pre-trained language model and fine-tune it, rather than training a new model from scratch on their dataset?
A development team is building a system to classify news articles into categories like 'Sports', 'Technology', and 'Politics'. They are using a modern approach that starts with a large, general-purpose language model. Arrange the following stages of their development process into the correct chronological order.
Traditional Role of Language Models
LLMs as Complete Systems in Generative AI