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This paper introduces Hallucination Self-Play (HSP), a framework that enhances the detection of faithfulness hallucinations in LLM outputs by enabling an iterative relationship between a detector and an evolved generator. By employing reinforcement learning from AI feedback (RLAIF), the detector is fine-tuned on human-labeled data and then used to train the generator, which produces increasingly challenging hallucinated responses. Experimental results on the RAGTruth benchmark show that HSP can significantly improve a small LLM's performance, allowing it to match or exceed that of larger models without requiring additional external supervision.
A small LLM can be trained to detect hallucinations as effectively as larger models through an innovative self-play framework that evolves its own training data.
Identifying faithfulness hallucinations in LLM-generated outputs remains challenging due to the scarcity of high-quality annotated data. Recent work relies on advanced LLMs to synthesize training data, including rationales, labels, and hallucinated claims. However, these methods treat the generator as a static component, limiting iterative improvement of the detector. To address this limitation, we introduce Hallucination Self-Play (HSP), a novel framework that enables the detector to bootstrap with an evolved generator. HSP involves two roles initialized from the same base model, a detector that assesses the faithfulness of model outputs, and a generator that produces increasingly hard-to-detect hallucinated responses. Specifically, the detector is first fine-tuned on human-labeled data and then employed as a reward model to train the generator via reinforcement learning from AI feedback (RLAIF). In turn, the evolved generator synthesizes hallucination data to further optimize the detector through rule-based reinforcement learning. Experiments on RAGTruth benchmark and two model families demonstrate that the proposed framework can progressively enhance a small LLM to match or even outperform advanced LLMs without external supervision. Our code is available at https://anonymous.4open.science/r/Hallucination-Self-Play-50B5 .
