LoGU: Long-form Generation with Uncertainty Expressions

The article introduces Long-form Generation with Uncertainty (LoGU), a new task aimed at improving LLMs' ability to express uncertainty in longer responses, thereby reducing hallucinations. It proposes a two-stage training framework using data refinement, supervised fine-tuning, and preference optimization, which significantly enhances factual accuracy and response quality in long-form generation.