
Language-model gains may come from diversity, not hidden experts
Randomly nudging a language model’s output scores can rival perturbing its weights on reasoning tasks, a study finds. The results challenge the idea that such gains require a hidden thicket of task-specific experts near the original model.
- language models
- neural thickets
- logit perturbation







