Prompt Engineering 简明教程

Transformer Architecture − Pre-training of language models is typically accomplished using transformer-based architectures like GPT (Generative Pre-trained Transformer) or BERT (Bidirectional Encoder Representations from Transformers). These models utilize self-attention mechanisms to effectively capture contextual dependencies in natural language.

Pre-training Objectives − During pre-training, language models are exposed to vast amounts of unstructured text data to learn language patterns and relationships. Two common pre-training objectives are − Masked Language Model (MLM) − In the MLM objective, a certain percentage of tokens in the input text are randomly masked, and the model is tasked with predicting the masked tokens based on their context within the sentence. Next Sentence Prediction (NSP) − The NSP objective aims to predict whether two sentences appear consecutively in a document. This helps the model understand discourse and coherence within longer text sequences.

Knowledge Transfer − Pre-training language models on vast corpora enables them to learn general language patterns and semantics. The knowledge gained during pre-training can then be transferred to downstream tasks, making it easier and faster to learn new tasks.

Reduced Data Requirements − Transfer learning reduces the need for extensive task-specific training data. By fine-tuning a pre-trained model on a smaller dataset related to the target task, prompt engineers can achieve competitive performance even with limited data.

Faster Convergence − Fine-tuning a pre-trained model requires fewer iterations and epochs compared to training a model from scratch. This results in faster convergence and reduces computational resources needed for training.

Feature Extraction − One transfer learning approach is feature extraction, where prompt engineers freeze the pre-trained model’s weights and add task-specific layers on top. The task-specific layers are then fine-tuned on the target dataset.

Full Model Fine-Tuning − In full model fine-tuning, all layers of the pre-trained model are fine-tuned on the target task. This approach allows the model to adapt its entire architecture to the specific requirements of the task.

Task-Specific Data Augmentation − To improve the model’s generalization on specific tasks, prompt engineers can use task-specific data augmentation techniques. Augmenting the training data with variations of the original samples increases the model’s exposure to diverse input patterns.

Domain-Specific Fine-Tuning − For domain-specific tasks, domain-specific fine-tuning involves fine-tuning the model on data from the target domain. This step ensures that the model captures the nuances and vocabulary specific to the task’s domain.

Data Preprocessing − Ensure that the data preprocessing steps used during pre-training are consistent with the downstream tasks. This includes tokenization, data cleaning, and handling special characters.

Prompt Formulation − Tailor prompts to the specific downstream tasks, considering the context and user requirements. Well-crafted prompts improve the model’s ability to provide accurate and relevant responses.