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cs.AI2026
Evaluating LLM Uncertainty in Long-Form Generation Using Deterministic Ground Truth
Ido Amit, Ido Galil, Ran El-Yaniv
As LLMs generate increasingly long outputs, effective uncertainty estimation must identify errors at fine-grained levels rather than discard entire responses. While such methods ex…
cs.AI2026
When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation
Shani Goren, Ido Galil, Ran El-Yaniv
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…