基于大模型标签蒸馏的搜索意图识别OA

In search engines,accurately recognizing the intent of user queries is crucial for improving search experience.Search intent recognition belongs to the task of short text classification.Traditional methods rely on massive manually labeled data,which implies high costs and difficulty in adapting to the rapid emergence of new intents.This paper proposes a search intent recognition method based on large model label distillation.It utilizes the powerful semantic understanding capabilities of Large Language Models(such as GPT4o,DeepSeek-R1,and Spark x1)to generate high-quality intent labels for unlabeled query instructions and construct training datasets.Furthermore,through Knowledge Distillation technology,the knowledge of large models is transferred to lightweight pre-trained models(such as ERNIE 3.0 and BERT)for fine-tuning.Experimental results show that this method significantly improves model performance on a Chinese dataset with a scale of 136 000,and effectively enhances intent recognition efficiency while reducing labeling costs.