90% Accuracy: Pre-Trained Language Models Revolutionize Regression

Pre-trained language models (LMs) are now being adapted for regression tasks, achieving up to 90% accuracy in evaluating model predictions. These models, traditionally used for text generation, are proving effective in tasks like scoring the quality of predictions in natural language processing (NLP). In a recent study, a regression metric was trained to assess the performance of models on under-represented languages. The dataset used included textual inputs paired with binary labels indicating whether the prediction was good (1) or bad (0). This approach not only enhances the evaluation of model outputs but also opens new avenues for applying LMs in various textual tasks, demonstrating their versatility beyond mere generation.

Source: towardsdatascience.com