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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.
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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
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
Controlled Text Generation Using Pre-Trained Language Models