LoGU: Long-form Generation with Uncertainty Expressions

Understand

While Large Language Models (LLMs) demonstrate impressive capabilities, they still struggle with generating factually incorrect content (i.e., hallucinations).

• A promising approach to mitigate this issue is enabling models to express uncertainty when unsure.
• Previous research on uncertainty modeling has primarily focused on short-form QA, but realworld applications often require much longer responses.
• In this work, we introduce the task of Long-form Generation with Uncertainty(LoGU).