Improving optimal prompt learning through multilayer fusion and latent dirichlet allocation

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Few-shot learning (FSL) leverages pre-trained models and prompt-based techniques to reduce fine-tuning needs.
• Challenges in FSL include prompt optimization and data scarcity in specialized domains.

Purpose of the Study:

• To develop a novel framework for enhanced few-shot learning.
• To address prompt optimization and data scarcity challenges in specialized domains.

Main Methods:

• A novel framework integrating a Global Attention Mechanism (GAM) with pre-trained language model features.
• Enhancement of prompt optimization using Latent Dirichlet Allocation (LDA) generated topic features.
• Integration of GAM with layer-specific features and LDA topics for latent information extraction.

Main Results:

• The proposed framework consistently outperforms state-of-the-art baselines across four datasets.
• Significant improvements were observed in specialized domains, particularly in therapeutic dialogue classification.
• GAM effectively integrates multi-layer features, enhanced by LDA topics, for superior few-shot learning performance.

Conclusions:

• The novel framework effectively addresses key challenges in few-shot learning.
• The integration of GAM and LDA offers a powerful approach for prompt optimization and feature extraction.
• This method shows strong potential for applications in specialized domains, including clinical settings.