When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation 事件

When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation arXiv:2602.11908v3 Announce Type: replace-cross Abstract: LLMs are widely used, yet they remain prone to factual errors that erode user trust and limit adoption in high-risk settings. One approach to mitigate this risk is to equip models with uncertainty estimation mechanisms that abstain when confidence is low. However, this binary "all-or-nothing" approach is excessively restrictive in long-form se