Research on abstract generation methods of large language models in low-resource scenarios optimized based on computer technology

Abstract

This paper focuses on the text generation task in the field of computer natural language processing, and studies the fast recognition method of large language models in low-resource scenarios. It mainly addresses the low-resource problems such as incomplete annotated data, domain mismatch, and weak model generalization. Firstly, it analyzes the problem of abstract enhancement in low-resource scenarios, and compares the computational adaptability between traditional extractive or generative methods and large language models. Secondly, four key technical schemes oriented to computer model optimization are proposed, including prompt engineering enhancement, parameter-efficient fine-tuning, data augmentation, and semi-supervised pseudo-labeling. Finally, experiments are carried out on scientific and technological, legal, and medical datasets under three low-resource settings: few-shot, zero-shot, and cross-domain. Experimental results show that the proposed computer technology optimization strategy can improve the model convergence speed and generalization ability, with the ROUGE-L index increased by up to 12.6 percentage points, indicating that the technical improvement is effective. This paper can provide a reference for the computer technology optimization of natural language processing tasks in low-resource scenarios.