Automated Prompt Generation in Semi-Supervised Learning

Prompt-based learning methods in semi-supervised learning (SSL) have gained attention for their effectiveness across various natural language understanding (NLU) tasks. However, creating multiple prompts and verbalizers manually requires significant effort and expertise, making it hard to implement across different datasets. This paper presents two methods to automate the design of prompts and the integration of verbalizers in SSL settings while maintaining performance.

#Methods

#Continuous Prompt Design

We propose using various Demonstration Examples and learnable prompt tokens to create diverse prompts. This method replaces manual prompt design with an automated process, allowing flexibility in SSL tasks.

1. Demonstration Examples: We add diverse examples to the prompt to show the model how to respond. This helps reduce the gap between examples and the actual tasks.
2. Soft Tokens Variation: By changing the number of prompt tokens, we encourage the model to learn different aspects of the language from the training data.

#Automatic Verbalizers

We replace manual verbalizers with automatic ones to streamline the process. We focus on:

• Prototypical Verbalizers: These learn from examples and assign class labels based on learned patterns.

#Training Pipeline

The training pipeline integrates automatic prompts and verbalizers with the existing Pattern-exploiting Training (PET) framework. We transform input sequences into a format suitable for training, where the model must predict masked tokens.

#Training Procedure

1. Labeler Models: Several models are trained using labeled data to create soft labels for a large amount of unlabeled data.
2. Final Classifier: After obtaining soft labels, we fine-tune a pre-trained language model for classification.

#Experiments

#Datasets

We tested our methods on several datasets: AG's News, Yahoo Answers, MNLI, RTE, and CB. Each dataset serves different classification needs, ranging from topic classification to textual entailment.

#Proposed Models

1. Demo+Soft Tokens PET: This model combines demonstration examples with continuous tokens.
2. Vary Soft Tokens PET: This model adjusts the number of continuous tokens for greater diversity.

#Results

Our methods outperform previous state-of-the-art methods that relied on manual prompts and verbalizers. Average accuracy across tasks shows significant improvement, demonstrating the effectiveness of our automated approach.

#Analysis

#Impact of SSL

Our experimental results indicate substantial benefits of using SSL methods over traditional supervised approaches. The introduction of diversity through multiple prompts enhances the learning process.

#Importance of Diversity in Prompts

We further analyzed the role of diverse prompts by comparing performance across various setups. Results indicate that a greater variety of prompts leads to improved outcomes.

#Baseline Comparisons

We compared our models with manual approaches and baseline models, demonstrating that our automated methods can match or exceed their performance without requiring human effort.

#Future Work

Moving forward, we aim to explore the potential of freezing model parameters for more efficient training and to expand our methods to other languages beyond English. We also plan to refine how we select demonstration examples to optimize model training.

#Conclusion

In summary, our research reveals that automated prompt and verbalizer generation in SSL can yield competitive results while significantly reducing the need for human design input. This work paves the way for more scalable and efficient natural language processing frameworks.

#References

(References were originally included but will not be displayed here to maintain simplicity.)