MPI-CD: Multi-Path Information Contrastive Decoding for Mitigating Hallucinations in Large Vision-Language Models

In recent years, despite substantial advancements in large vision-language models (LVLMs), they still encounter the issue of ''hallucinations''-where generated results appears reasonable but often deviates from the visual input or actual facts. In contrast, the human cognitive system, when processing visual input, initially relies on visual perception to distinguish between the salient region and non-salient region, integrating relevant information. Subsequently, it recalls pertinent memory details, ultimately generating a comprehensive cognitive outcome. Inspired by this process, we propose a novel, training-free decoding approach, dubbed as Multi-Path Information Contrastive Decoding (MPI-CD). Specifically, to simulate the human information integration process, we design a three-branch structure called the Tri-Branch Integrator (TBI), which contrasts the original, salient region, and non-salient region images to effectively improve the reliability of the LVLMs' output. Furthermore, to mimic the human memory recall mechanism, we further investigate the importance of hidden layer features and propose the Memory Recall Module (MRM). This module adaptively extracts meaningful memory information from the hidden layers and incorporates it into the decoding process, thereby effectively alleviating the hallucination issue. We conduct extensive experiments on three widely used benchmarks (e.g. POPE, AMBER, and MME) using two classic LVLMs. The experimental results demonstrate that our MPI-CD significantly mitigates hallucinations in LVLMs without requiring additional training.