Arbitrary Natural Language Text

Research on arbitrary natural language text focuses on generating and manipulating text from diverse and unstructured inputs, moving beyond predefined labels or formats. Current efforts involve developing models that can handle this complexity, employing techniques like large language models, transformers, and neural implicit surfaces, often incorporating innovative training methods such as round-trip translations and adversarial learning. This work is significant for advancing natural language processing capabilities, enabling applications like realistic text-to-motion synthesis, improved text recognition in challenging scenarios, and more efficient large language model editing.