Neural Diversity Reduces Hallucinations in Language Models

A recent paper published on arXiv (2510.20690) introduces the concept of neural diversity—decorrelated parallel representations—as a systematic approach to minimize hallucination occurrences in language models while maintaining fixed parameters and data limits. This research presents the first formal tail bounds for the probability of hallucination in ensemble language models, reinterpreting it as a second-moment reliability issue and accounting for 94.3% of the variability in empirical reliability across different parallel setups. The authors propose ND-LoRA (Neural Diversity Low-Rank Adaptation), which integrates parallel LoRA adapters with Barlow Twins regularization, achieving a reduction in hallucinations of up to 25.6% (14.6% on average) without compromising overall accuracy. Ablation studies indicate a synergistic effect between LoRA adapters and regularization, while causal interventions confirm neurodiversity as the key mediating element. The paper can be accessed on arXiv with the identifier 2510.20690, categorized as 'replace-cross'.