Text Generation Problem

Text generation aims to create human-like text from various inputs, focusing on producing coherent, meaningful, and stylistically appropriate outputs. Current research emphasizes addressing challenges like generating text under strict constraints, achieving diverse high-quality outputs using quality-diversity algorithms and AI feedback, and improving unsupervised style transfer techniques through contrastive learning and refined gradient-based methods. These advancements leverage pretrained language models (PLMs) and incorporate techniques from constraint programming and graph generation, significantly impacting fields like creative writing, clinical research, and drug discovery.