Prompting in Language Models
Prompting is a technique for guiding a pre-trained language model to perform a specific task by structuring its input as a textual instruction or query. This method leverages the model's existing knowledge to generate desired outputs without needing to update its parameters through retraining. It is a foundational mechanism that enables advanced application strategies such as zero-shot and few-shot learning.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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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
Zero/Few-Shot Learning
A team is tasked with adapting a large, pre-trained language model to summarize legal documents. One developer designs a method where each summarization request includes a detailed set of instructions and examples of high-quality summaries, which are provided to the original, unchanged model. Another developer uses a large dataset of legal documents and their corresponding summaries to make small, permanent adjustments to the model's internal configuration before deploying it. What is the most significant difference between these two approaches regarding the pre-trained model itself?
Choosing a Model Adaptation Strategy
Key Areas of Prompt Engineering
Instruction-Following Ability of LLMs
Components of a Prompt: Instruction and User Input
When a language model successfully performs a new task based on a well-crafted prompt, its internal parameters are temporarily adjusted for the duration of that specific task to better align with the provided instructions.
Prompting as a Text Generation Task