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 HALLUCINATION-R1, a robustness-oriented paraphrase generation framework that learns to produce semantically f...
| Authors | Wenhan Yu, Wenxin Wu, Hao Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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