Title: Building Reliable Long-Form Generation via Step-Wise Hallucination Rejection Sampling
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: hallucination, inferece-time scaling, large language models, semantic entropy

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 address this challenge, we propose a novel inference-time scaling framework, named Step-wise HAllucination Rejection Sampling (SHARS), that allocates additional computation during decoding to detect and reject hallucinated content as it is produced. By retaining only confident information and building subsequent generations upon it, the framework mitigates hallucination accumulation and enhances factual consistency. To instantiate this framework, we further introduce a new uncertainty-based hallucination detection method, named HalluSE, for long-form generation, improving upon the prior semantic entropy approach. The combined system enables models to self-correct hallucinations without requiring external resources such as web search or knowledge bases, while remaining compatible with them for future extensions. Empirical evaluations on standardized hallucination benchmarks demonstrate that our method substantially reduces hallucinations in long-form generation while preserving or even improving the informativeness of generation.

TL;DR: an inference-time scaling framework for hallucination mitigation in open-ended generation.