Hallucination detection in LLMs often relies on output consistency or model-internal signals. While output-based methods have the advantage of applicability to closed-source models, existing studies often overlook a key limitation: they struggle on tasks that admit multiple valid answers, where variation across generations may reflect legitimate alternative responses rather than hallucinations. In this paper, we propose a question-type-aware hallucination detection framework that distinguishes single-answer and multi-answer regimes. Given a predicted question-type signal, we derive type-conditioned diagnostic features from low- and high-temperature generations, and build two alternative hallucination detectors based on these features: a calibrated type-aware uncertainty estimator and a feature-verbalized few-shot LLM judge. Experiments on NQ-Open, WebQA, and AmbigQA across both open-source and closed-source LLMs demonstrate that our method consistently outperforms conventional semantic clustering approaches and representative baselines in hallucination detection.

In retrieval-augmented generation (RAG) question answering systems, generating citations for large language model (LLM) outputs enhances verifiability and helps users identify potential hallucinations. Existing citation generation methods for retrieval-augmented generation usually provide evidence at the paragraph or sentence level. Although such citations improve verifiability, they may still impose unnecessary reading burden on users: a cited sentence can contain irrelevant clauses, while an answer-bearing clause may lack sufficient contextual information such as the referred subject. In this paper, we study concise and sufficient sub-sentence citation generation, aiming to identify minimal yet self-contained evidence spans for answer verification. To this end, we first develop annotation guidelines for such citations and construct a corresponding dataset. To reduce annotation cost, we propose a data augmentation framework that generates synthetic citation annotations and filters low-quality instances using an evaluator model. Our experiments on the constructed dataset demonstrate that the proposed approach can generate high-quality and more readable citations. The code and data will be publicly released.