Improving Text Generation for Product Description by Label Error Correction

Text generation is an important method to generate accurate and available product description from product title.Product description generation's main problem for online E-commerce application is the available rate of generated text.The available rate of online deployment standard needs to reach above 99%.Model-centric method is limited by the quality of the training dataset.To handle the problem, we propose our data-centric method to improve the generation model's available rate from 88.0% to 99.2%.Our approach helps in building models using LLMs (large language models) annotation results and constructing datasets to obtain better results than LLMs.Also, our method simplifies the human labeling work to 2-class choices to label, which improve the labeling speed.In summary, our method saves about 10x of the labeling time and achieves 99.2% accuracy to be deployed online.