arxiv
PublishedApril 30, 2026 at 4:00 AM
▲bullish
Delineating Knowledge Boundaries for Honest Large Vision-Language Models
Publisher summary· verbatim
arXiv:2604.26419v1 Announce Type: cross Abstract: Large Vision-Language Models (VLMs) have achieved remarkable multimodal performance yet remain prone to factual hallucinations, particularly in long-tail or specialized domains. Moreover, current models exhibit a weak capacity to refuse queries that
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models19harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders19harxivData Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA19hThe Bubble Brief
WEEKLYRead computer-vision insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗