Learning to Reason for Hallucination Span Detection

Over the past few years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks (Xie et al., 2023; Zhang et al., 2023; Gao et al., 2024; OpenAI et al., 2024). However, they are still prone to generating hallucinations--content that is not supported by the input context or the underlying knowledge sources (Zhu et al., 2024; Kalai et al., 2025; Huang et al., 2025). Hallucinations pose critical risks in downstream applications such as summarization and long-form question answering, where reliability and factual consistency with respect to the input context are paramount. Hence, the ability to detect hallucinations is crucial for successful real-world deployment of LLMs. Most existing research works focus on binary hallucination detection problem, where the goal is to determine if the model output contains hallucinations or not (Yang et al., 2024a,b; Tang et al., 2024; Ravi et al., 2024; Ji et al., 2024; Chuang et al., 2024). While useful, this formulation is limited: in many real-world applications, one often needs to know which specific spans in the model output are hallucinated in order to assess the reliability of the generated content. This motivates the problem of hallucination span detection, where the goal is to precisely locate unsupported content in the model output (Wu et al., 2023; Ogasa and Arase, 2025). Recently, reasoning--the process of systematically arriving at conclusions by generating and utilizing intermediate steps--has been shown to significantly enhance the capabilities of LLMs in solving complex tasks such as mathematics (Shao et al., 2024; Yu et al., 2025) and coding (Liu and Zhang, 2025; Chen et al., 2025). Hallucination span detection is also a complex multi-step decision making process as it requires carefully analyzing the model output to extract all the stated facts and verifying whether each of these facts is fully supported by the input context, and could benefit significantly from a learned reasoning process.