Building Reliable Long-Form Generation via Hallucination Rejection Sampling 事件

Building Reliable Long-Form Generation via Hallucination Rejection Sampling arXiv:2606.03628v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress in open-ended text generation, yet they remain prone to hallucinating incorrect or unsupported content, which undermines their reliability. This issue is exacerbated in long-form generation due to hallucination snowballing, a phenomenon where early errors propagate and compound into subsequent outputs. To addre