Table to Text

Table-to-text generation focuses on automatically converting tabular data into natural language descriptions, aiming to improve data accessibility and understanding. Current research emphasizes improving the factual accuracy and fluency of generated text, exploring various model architectures including diffusion models, large language models (LLMs), and sequence-to-sequence models, often incorporating techniques like prompt engineering and knowledge adaptation to enhance performance. This field is significant for its potential to automate data summarization and improve human-computer interaction across diverse applications, from data analysis to question answering systems.