DeP mitigates MLLM hallucinations by dynamically perturbing text prompts to identify and reinforce stable visual evidence regions while counteracting language prior biases using attention variance and logit statistics.
Truthprint: Miti- gating lvlm object hallucination via latent truthful-guided pre- intervention
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3representative citing papers
VCE mitigates object hallucination in LVLMs by decomposing activation patterns from contrastive visual inputs via SVD to suppress hallucination subspaces through targeted parameter edits.
IUQ quantifies claim-level uncertainty in long-form LLM generation by combining inter-sample consistency and intra-sample faithfulness through an interrogate-then-respond approach and outperforms baselines on two datasets.
citing papers explorer
-
Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation
DeP mitigates MLLM hallucinations by dynamically perturbing text prompts to identify and reinforce stable visual evidence regions while counteracting language prior biases using attention variance and logit statistics.
-
VCE: A zero-cost hallucination mitigation method of LVLMs via visual contrastive editing
VCE mitigates object hallucination in LVLMs by decomposing activation patterns from contrastive visual inputs via SVD to suppress hallucination subspaces through targeted parameter edits.
-
IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation
IUQ quantifies claim-level uncertainty in long-form LLM generation by combining inter-sample consistency and intra-sample faithfulness through an interrogate-then-respond approach and outperforms baselines on two datasets.