RCSP trains soft prompts with contrastive loss, curriculum learning, and KL regularization to balance hallucination suppression, abstention, and factual recall, yielding higher F-scores than baselines on five QA datasets using Gemma 3 (12B) and Llama 3.1 (8B) backbones while updating only a small fr
arXiv preprint arXiv:2503.01332 (2025)
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Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models
RCSP trains soft prompts with contrastive loss, curriculum learning, and KL regularization to balance hallucination suppression, abstention, and factual recall, yielding higher F-scores than baselines on five QA datasets using Gemma 3 (12B) and Llama 3.1 (8B) backbones while updating only a small fr