Neural Text Generation

Neural text generation aims to create human-quality text using artificial neural networks, focusing on improving fluency, diversity, and controllability of the generated output. Current research emphasizes developing more efficient decoding algorithms (like contrastive search and best-k search) and incorporating constraints (lexical, structural, or relation-based) to guide generation, often leveraging large language models. These advancements are significant for various applications, including machine translation, summarization, and dialogue systems, by enabling more accurate, diverse, and controllable text generation.