Hallucination Mitigation

Hallucination mitigation studies how to keep generative models' claims aligned with evidence from text, images, audio, video, structured knowledge, or physical observations, especially when inputs are ambiguous or incomplete. Common approaches include retrieval and evidence routing, claim-level verification and refinement, abstention or confidence calibration, targeted training, and inference-time methods such as contrastive decoding, activation steering, and representation edits. Methods are evaluated with factuality and groundedness benchmarks across language, dialogue, multimodal question answering, medical imaging, speech, and generative reconstruction, often measuring unsupported claims alongside accuracy and task quality. Key tensions include reducing errors without suppressing correct content or degrading fluency, controlling compute and latency, and distinguishing genuine hallucination from misunderstanding; detection and localization can also be easier than reliable correction.