Transfer knowledge of a PTM to the downstream NLP tasks
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Choose appropriate pre-training task, model architecture and corpus. Currently, the language model is the most popular pretraining task and can more efficiently solve a wide range of NLP problems. However, different pre-training tasks have their own bias and give different effects for different tasks. Besides, the data distribution of the downstream task should be approximate to PTMs.
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Choose appropriate layers. Given a pre-trained deep model, different layers should capture different kinds of information, such as POS tagging, parsing, long-term dependencies, semantic roles, coreference.
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There are two common ways of model transfer: feature extraction (where the pre-trained parameters are frozen), and fine-tuning (where the pre-trained parameters are unfrozen and fine-tuned). In feature extraction way, the pre-trained models are regarded as off-the-shelf feature extractors. Moreover, it is important to expose the internal layers as they typically encode the most transferable representations.
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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