ArticleFrontiers in robotics and AI2025
Improving optimal prompt learning through multilayer fusion and latent dirichlet allocation.
Article in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Recent advances in few-shot learning have demonstrated the potential of prompt-based techniques with pre-trained models, eliminating the need for extensive fine-tuning. However, challenges such as obtaining optimal prompts and addressing data scarcity in specialized domains remain challenging. We introduce a novel framework incorporating a Global Attention Mechanism (GAM) that effectively integrates features from multiple layers of pre-trained language models, enhanced by Latent Dirichlet Allocation (LDA) generated topic features for prompt optimization. Extensive experiments on four datasets consistently show that our approach outperforms state of-the-art baselines. The strategic integration of GAM with layer-specific features and LDA topics proves particularly effective in extracting valuable latent information for few-shot learning scenarios, yielding significant improvements in specialized domains, as evidenced by enhanced performance in therapeutic dialogue classification within a Applied Behavior Analysis clinical dataset.
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