A Unified Framework for NLP Tasks by ReLabel Method

[TechRxiv] Tong Guo proposes a unified framework called ReLabel for identifying noisy data in deep learning applications and relabeling it, using model predictions as a reference during relabeling. The paper states that initial datasets come from manual annotation, large language model generation, or user behavior logs, all of which may contain a certain amount of noisy data; the relabeling process can be done manually or with the help of large language models for annotation. The framework covers multiple types of tasks, including classification, sequence labeling, object detection, sequence generation, and click-through rate prediction. The author states that evaluation results on the development set and manual evaluation results validate this approach, and mentions that the goal is to achieve a score above 90 on the development set.