ACT Now: Preempting LVLM Hallucinations via Adaptive Context Integration
This addresses hallucination issues in LVLMs, offering a robust and adaptable solution for improving vision-language alignment, though it is incremental as it builds on existing mitigation strategies.
The paper tackles the problem of severe hallucination in Large Vision-Language Models (LVLMs) by proposing Adaptive Context Integration (ACT), a training-free inference method that adaptively integrates contextual information, resulting in significant reductions in hallucinations and competitive performance on benchmarks.
Large Vision-Language Models (LVLMs) frequently suffer from severe hallucination issues. Existing mitigation strategies predominantly rely on isolated, single-step states to enhance visual focus or suppress strong linguistic priors. However, these static approaches neglect dynamic context changes across the generation process and struggles to correct inherited information loss. To address this limitation, we propose Adaptive Context inTegration (ACT), a training-free inference intervention method that mitigates hallucination through the adaptive integration of contextual information. Specifically, we first propose visual context exploration, which leverages spatio-temporal profiling to adaptively amplify attention heads responsible for visual exploration. To further facilitate vision-language alignment, we propose semantic context aggregation that marginalizes potential semantic queries to effectively aggregate visual evidence, thereby resolving the information loss caused by the discrete nature of token prediction. Extensive experiments across diverse LVLMs demonstrate that ACT significantly reduces hallucinations and achieves competitive results on both discriminative and generative benchmarks, acting as a robust and highly adaptable solution without compromising fundamental generation capabilities.