arXiv:2507.20546v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. As users increasingly encounter such black-box outputs, detecting hallucinations has become a critical challenge. To address this challenge, we focus on developing a hallucination detection framework for black-box generators. Motivated by the observation that hallucinations, once introduced, tend to persist, we sample future contexts. The sampled future contexts provide valuable clues for hallucination detection and can be effectively integrated with various sampling-based methods. We extensively demonstrate performance improvements across multiple methods using our proposed sampling approach.
Identifying needs in adult rehabilitation to support the clinical implementation of robotics and allied technologies: an Italian national survey
IntroductionRobotics and technological interventions are increasingly being explored as solutions to improve rehabilitation outcomes but their implementation in clinical practice remains very limited. Understanding patient