Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Space
arXiv:2602.23753v1 Announce Type: new Abstract: This study addresses the issues of semantic entanglement, unclear label structure, and insufficient feature representation in few-shot text classification, and proposes an optimization framework based on structured prompts to enhance semantic understanding and task adaptation under low-resource conditions. The framework first uses a pretrained language model to encode the input text and obtain basic semantic representations. It then introduces structured prompts composed of multi-dimensional semantic factors and integrates them with text features through a learnable combination mechanism, which forms task-related representations with clear boundaries in the latent space. To further strengthen the consistency between text representations and label semantics, the method constructs a structured label embedding matrix and employs a cross-space alignment mechanism to ensure stable matching between textual features and label attributes. In add
arXiv:2602.23753v1 Announce Type: new Abstract: This study addresses the issues of semantic entanglement, unclear label structure, and insufficient feature representation in few-shot text classification, and proposes an optimization framework based on structured prompts to enhance semantic understanding and task adaptation under low-resource conditions. The framework first uses a pretrained language model to encode the input text and obtain basic semantic representations. It then introduces structured prompts composed of multi-dimensional semantic factors and integrates them with text features through a learnable combination mechanism, which forms task-related representations with clear boundaries in the latent space. To further strengthen the consistency between text representations and label semantics, the method constructs a structured label embedding matrix and employs a cross-space alignment mechanism to ensure stable matching between textual features and label attributes. In addition, the model applies prompt orthogonality constraints and a joint optimization objective to maintain independence across different semantic factors in the prompts, allowing the structured prompts to provide transparent and controllable guidance for classification decisions. Three types of sensitivity experiments, including learning rate sensitivity, prompt length sensitivity, and data scale sensitivity, are designed to evaluate the stability and robustness of the framework under different conditions. Experimental results show that the proposed structured prompt optimization framework effectively alleviates semantic conflicts and label ambiguity in few-shot text classification. It significantly improves performance on accuracy, precision, recall, and AUC, and demonstrates strong cross-task applicability.
Executive Summary
This article proposes a novel framework for few-shot text classification, utilizing structured prompts to enhance semantic understanding and task adaptation under low-resource conditions. The framework integrates multi-dimensional semantic factors with text features, ensuring clear boundaries in the latent space. Experimental results demonstrate significant improvements in accuracy, precision, recall, and AUC, showcasing the framework's effectiveness in alleviating semantic conflicts and label ambiguity.
Key Points
- ▸ Introduction of structured prompts composed of multi-dimensional semantic factors
- ▸ Integration of text features with prompts through a learnable combination mechanism
- ▸ Employment of a cross-space alignment mechanism to ensure stable matching between textual features and label attributes
Merits
Improved Semantic Understanding
The framework's use of structured prompts enhances semantic understanding, allowing for more accurate text classification
Robustness to Low-Resource Conditions
The framework demonstrates strong performance under low-resource conditions, making it suitable for few-shot text classification tasks
Demerits
Computational Complexity
The framework's use of multi-dimensional semantic factors and learnable combination mechanisms may increase computational complexity
Dependence on Pretrained Language Models
The framework relies on pretrained language models, which may limit its applicability to domains with limited pretraining data
Expert Commentary
The proposed framework represents a significant advancement in few-shot text classification, demonstrating the potential of structured prompts to enhance semantic understanding and task adaptation. The framework's use of multi-dimensional semantic factors and cross-space alignment mechanisms provides a nuanced approach to text classification, allowing for more accurate and robust performance. However, further research is needed to address the framework's computational complexity and dependence on pretrained language models.
Recommendations
- ✓ Future research should focus on optimizing the framework's computational complexity, potentially through the use of more efficient combination mechanisms
- ✓ The framework should be applied to a wider range of text classification tasks to demonstrate its generalizability and robustness