Enhancing Chinese abbreviation prediction with LLM generation and contrastive evaluation

Abstract

Chinese abbreviation prediction plays an important role in natural language processing. The prevalent approach often utilizes generation models to predict abbreviations for full forms, but relying solely on a single generation model may not yield high-quality abbreviations. We emphasize the importance of introducing an evaluation model after the generation model to assess the rationality of generated abbreviations. Hence, in this paper, we propose a novel two-stage method with LLM generation and contrastive evaluation for Chinese abbreviation prediction. In the first stage, we design a type discriminator to determine the abbreviation type and then introduce a pre-trained and fine-tuned LLM to generate multiple candidate abbreviations. In the second stage, we propose a contrastive evaluation model to assess the rationality of the candidates based on the abbreviation scorer and phrase scorer with a joint learning strategy. Experiments on two public datasets indicate that our method outperforms the current state-of-the-art method, achieving improvements of 3.32% and 1.73%, respectively. More importantly, we deploy it on the Fliggy application and the 20-day online A/B testing shows a 0.65% increase in Point of Interest Recognition Rate and a 1.37% increase in Page View Click-Through Rate when using abbreviations predicted by our method in the search system.