KPLLM-STE: Knowledge-enhanced and prompt-aware large language models for short-text expansion

Hao Zhong, Qi Zhang, Weisheng Li, Ronghua Lin, Yong Tang

Abstract: Short-text Expansion plays a significant role in enhancing the quality, diversity, and practicality of Short-text, helping users to more comprehensively understand the content expressed in the Short-text. In this paper, we aim to enhance the capabilities of large language models in short-text expansion through knowledge graphs and propose the knowledge-enhanced and prompt-aware large language models. First, we construct a multi-dimensional knowledge graph that includes semantics, sentiment, and topics based on large language models in domain-specific text. Second, we propose a method for mining prompts of Short-text across the three dimensions of semantics, sentiment, and topics based on the constructed multi-dimensional knowledge graph. Finally, we match triplets in the constructed knowledge graph based on the generated prompts in the three dimensions. The matched triplets is then integrated by the large language model to generate a expansion of given short-text. Experiments are conducted using three large language models on two public datasets, and the results indicate that our model shows improvements across multiple metrics for text similarity, readability, and coherence compared to the short-text expansion generated by the baseline large language models and existing methods.