Applications of PTMs
Pre-trained models (PTMs) like BERT are generally well-suited for a variety of downstream NLP tasks. After a model has been fine-tuned for a specific purpose, it can be applied to that task. Some classic examples of such applications include:
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General Evaluation Benchmark
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Question Answering
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Sentiment Analysis
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Named Entity Recognition
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Machine Translation
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Data Science
Foundations of Large Language Models Course
Computing Sciences
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
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Transfer knowledge of a PTM to the downstream NLP tasks
Fine-Tuning Strategies
Applications of PTMs
Fine-tuning for Sequence Encoding Models
Fine-Tuning Pre-trained Models for Downstream Tasks
Freezing Encoder Parameters During Fine-Tuning
Discarding the Pre-training Head for Downstream Adaptation
Textual Instructions for Task Adaptation
Influence of Downstream Task on Model Architecture
Broad Applications of Fine-Tuning in LLM Development
Scope of Introductory Fine-Tuning Discussion
LLM Alignment
Pre-train and Fine-tune Paradigm for Encoder Models
Necessity of Fine-Tuning for Downstream Task Adaptation
Fine-Tuning as a Standard Adaptation Method for LLMs
Prompting in Language Models
Fine-Tuning as a Mechanism for Activating Pre-Trained Knowledge
A startup wants to adapt a large, pre-trained language model to classify customer sentiment (positive, negative, neutral). They have a very small labeled dataset (fewer than 500 examples) and extremely limited access to high-performance computing, making extensive retraining financially unfeasible. Which adaptation approach is most suitable for their situation?
Efficiency of LLM Adaptation via Prompting
A developer intends to specialize a general-purpose, pre-trained language model for a new text classification task by updating its internal parameters. Arrange the following steps in the correct chronological order to accomplish this adaptation.
Selecting an Adaptation Strategy for a Pre-trained Model
Learn After
General Evaluation Benchmark
Named Entity Recognition
Text Regression with BERT Models
Single-Text Classification with BERT Models
Selecting the Appropriate NLP Task for a Business Need
Match each description of a natural language processing task with the most appropriate application name.
A company uses a fine-tuned pre-trained model to automatically process thousands of customer product reviews. When a review states, 'I am extremely disappointed with this purchase; it stopped working after just one use,' the system assigns it a 'Negative' label. Which primary application of a pre-trained model does this system exemplify?