Prefill-Time Intervention (PTI) reduces hallucinations in large vision-language models by applying a one-time modality-aware steering correction to the initial KV cache at the prefill stage rather than during autoregressive decoding.
Amber: An llm-free multi- dimensional benchmark for mllms hallucination evaluation,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2representative citing papers
VLMs exhibit only slight performance degradation on hallucination benchmarks when substantial image tokens are removed, with layer-wise analysis showing increased visual token similarity in deeper layers, suggesting current benchmarks inadequately test fine-grained visual grounding.
citing papers explorer
-
Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models
Prefill-Time Intervention (PTI) reduces hallucinations in large vision-language models by applying a one-time modality-aware steering correction to the initial KV cache at the prefill stage rather than during autoregressive decoding.
-
Seeing without Looking: Do Vision-Language Benchmarks Really Test Vision?
VLMs exhibit only slight performance degradation on hallucination benchmarks when substantial image tokens are removed, with layer-wise analysis showing increased visual token similarity in deeper layers, suggesting current benchmarks inadequately test fine-grained visual grounding.