Eliciting Intrinsic Hallucinations in LLMs via Semantically Equivalent Adversarial Attacks
Large language models (LLMs) are often used in conjunction with external knowledge sources to improve their factual accuracy and decrease hallucinations, through methods such as Retrieval-Augmented Generation (RAG). However, these systems remain susceptible to intrinsic hallucinations, where the model generates unfaithful or fabricated information that is not supported by the retrieved evidence. We propose a novel framework to assess model robustness against this phenomenon by stress-testing using natural, semantically equivalent variations of a user query found via adversarial optimization methods. We apply our framework, which enforces strict semantic equivalence constraints and an intr...
| Authors | Atri Vivek Sharma, Brian Formento, Alessio Lomuscio |
|---|---|
| Primary category | cs.CL |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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