Building Reliable Long-Form Generation via Hallucination Rejection Sampling
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 address this challenge, we propose a novel inference-time hallucination mitigation framework, named Segment-wise HAllucination Rejection Sampling (SHARS), which uses an arbitrary hallucination detector to identify and reject hallucinated segments during generation and resample until faithful content is produced. By retaining ...
| Authors | Lin Li, Georgia Channing, Suhaas M Bhat et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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