Constructing Fine-Tuning Datasets for Bid-Generation-Model Using Title-Content Similarity

As AI-generated content and large-scale models evolve, fine-tuning models for specific vertical domains is crucial for improving performance. This research focuses on the bid generation field and proposes a method for constructing fine-tuning datasets based on title and content similarity. By analyzing the relationships between bid template titles and bid content in the dataset, a standardized process for building fine-tuning datasets is developed. The method uses word embedding, similarity calculations, and preprocessing techniques such as part-of-speech tagging and stop-word filtering. Experiments show that the proposed method significantly improves the accuracy, precision, and generalization of bid generation models. This work offers an automated dataset construction method for bid models and provides insights for fine-tuning in other domains.