SOURCE-LINKED INTELLIGENCE
Hallucination-R1: Robustness-Oriented Paraphrase Generation for Factual Consistency
Factual hallucination is commonly defined by incorrect factual outputs. We study a paraphrase-induced hallucination setting, where a model answers a factual question correctly in its original form but generates an incorrect answer under a semantically equivalent paraphrase. Such inconsistencies expose latent factual instability under semantic invariance. However, general-purpose paraphrases are often insufficient as robustness-oriented supervision: near-copy paraphrases provide weak signals, while overly diverse paraphrases may break semantic equivalence. In this paper, we propose HALLUCINATIO
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T02:16:54.000Z
First collected: 2026-09-23T14:01:59.594Z. This is not the publication date.