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The paper introduces SHAM-Bench, a unified span-level hallucination detection benchmark aggregating 6,461 human-annotated examples across six NLG tasks under a common schema and evaluation protocol. It finds that fine-tuned detectors degrade under distribution shift while prompt-based reasoning methods generalize better, and releases the dataset, code, and a public leaderboard.
SpanUQ introduces a lightweight probe for span-level uncertainty quantification in LLMs, using a DETR-style decoder and mixture of Beta distributions, achieving superior error localization and faster inference than sampling-based methods.
This paper introduces a unified benchmark for span-level hallucination detection in RAG systems that extends beyond natural language to code, tool output, and structured documents, and presents a fine-tuned Qwen3.5-2B detector that outperforms existing methods on these new domains while remaining competitive on standard NLP benchmarks.