LoGU: Long-form Generation with Uncertainty Expressions

While Large Language Models (LLMs) demonstrate impressive capabilities, they still struggle with hallucinations. A promising approach to mitigate hallucinations is enabling models to express uncertainty when unsure. Previous research on uncertainty estimation has primarily focused on short-form QA, but real-world applications often require much longer responses. In this work, we introduce the task of Longform Generation with Uncertainty (LoGU), which requires the models to explicitly express uncertainty during the generation. We identify two key challenges: Uncertainty Suppression, where models hesitate to express uncertainty, and Uncertainty Misalignment, where models convey uncertainty inaccurately. To tackle these challenges, we propose a novel decomposition-based data collection framework and a two-stage training pipeline. Specifically, we use supervised fine-tuning (SFT) for uncertainty suppression problem and direct preference optimization (DPO) for uncertainty misalignment. Experiments on three long-form datasets demonstrate the effectiveness of our approach, showing improvements in factual accuracy, reduction of incorrect statements, and preservation of the overall comprehensiveness of the generated responses. Further analysis reveals that baseline methods tend to express uncertainty in vague and broad terms, while our method generates more specific and targeted uncertainty expressions. 1