Fine-Tuning as a Mechanism for Activating Pre-Trained Knowledge
The pre-training and fine-tuning paradigm operates on the principle that LLMs acquire latent abilities for instruction comprehension and response generation during pre-training. However, these learned instruction-response mappings may not have a high probability of being generated during inference. Fine-tuning serves as a mechanism to activate these dormant capabilities by slightly adjusting the model's parameters using a small set of supervised data, which increases the likelihood of generating the desired responses to instructions.
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References
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Tags
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.4 Alignment - Foundations of Large Language Models
Related
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
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Efficiency of LLM Adaptation via Prompting
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Selecting an Adaptation Strategy for a Pre-trained Model
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Fine-Tuning as a Mechanism for Activating Pre-Trained Knowledge
Evaluating a Researcher's Conclusion on Model Training
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