Grounded Text Generation
Overview
Grounded text generation studies how language models can produce useful text whose claims and details are supported by source documents, databases, structured knowledge, or images and other observations. Common approaches include retrieval-augmented generation with citations, knowledge-graph and schema-constrained pipelines, planning with intermediate representations, and fine-tuning or decoding methods that promote evidence consistency; verification and human review are often added for high-stakes use. Evaluation spans factuality and citation accuracy, constraint satisfaction, task-specific quality, and robustness across long or multi-turn contexts, including summarization, question answering, dialogue, structured reporting, and multimodal tasks. The central challenge is balancing completeness and fluent, useful output against traceable support: retrieval or injected context can raise recall without guaranteeing reasoning, and unsupported details can still emerge as context grows or evidence is ambiguous.
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