Aligning Attention Distribution to Information Flow for Hallucination Mitigation in Large Vision-Language Models

Due to the unidirectional masking mechanism, Decoder-Only models propagate information from left to right. LVLMs (Large Vision-Language Models) follow the same architecture, with visual information gradually integrated into semantic representations during forward propagation. Through systematic analysis, we observe that over 80\% of the visual information is absorbed into the semantic representations. However, the model's attention still predominantly focuses on the visual representations. This misalignment between the attention distribution and the actual information flow undermines the model's visual understanding ability and contributes to hallucinations. To address this issue, we enhance the model's visual understanding by leveraging the core information embedded in semantic representations. Specifically, we identify attention heads that focus on core semantic representations based on their attention distributions. Then, through a two-stage optimization paradigm, we propagate the advantages of these attention heads across the entire model, aligning the attention distribution with the actual information flow. We evaluate our method on three image captioning benchmarks using five different LVLMs, demonstrating its effectiveness in significantly reducing hallucinations. Further experiments reveal a trade-off between reduced hallucinations and richer details. Notably, our method allows for manual adjustment of the model's conservativeness, enabling flexible control to meet diverse real-world requirements. Code will be released once accepted.

Mitigate Language Priors in Large Vision-Language Models by Cross-Images Contrastive Decoding

Language priors are a major cause of hallucinations in Large Vision-Language Models (LVLMs), often leading to text that is linguistically plausible but visually inconsistent. Recent work explores contrastive decoding as a training-free solution, but these methods typically construct negative visual contexts from the original image, resulting in visual information loss and distorted distribution. Motivated by the observation that language priors stem from the LLM backbone and remain consistent across images, we propose Cross-Images Contrastive Decoding (CICD), a simple yet effective training-free method that uses different images to construct negative visual contexts. We further analyze the cross-image behavior of language priors and introduce a distinction between essential priors (supporting fluency) and detrimental priors (causing hallucinations), enabling selective suppression. By selectively preserving essential priors and suppressing detrimental ones, our method reduces hallucinations while maintaining coherent and fluent language generation. Experiments on four benchmarks and six LVLMs across three model families confirm the effectiveness and generalizability of CICD, especially in image captioning, where language priors are particularly pronounced. Code will be released upon acceptance.

Opening the black box of deep learning

The great success of deep learning shows that its technology contains profound truth, and understanding its internal mechanism not only has important implications for the development of its technology and effective application in various fields, but also provides meaningful insights into the understanding of human brain mechanism. At present, most of the theoretical research on deep learning is based on mathematics. This dissertation proposes that the neural network of deep learning is a physical system, examines deep learning from three different perspectives: microscopic, macroscopic, and physical world views, answers multiple theoretical puzzles in deep learning by using physics principles. For example, from the perspective of quantum mechanics and statistical physics, this dissertation presents the calculation methods for convolution calculation, pooling, normalization, and Restricted Boltzmann Machine, as well as the selection of cost functions, explains why deep learning must be deep, what characteristics are learned in deep learning, why Convolutional Neural Networks do not have to be trained layer by layer, and the limitations of deep learning, etc., and proposes the theoretical direction and basis for the further development of deep learning now and in the future. The brilliance of physics flashes in deep learning, we try to establish the deep learning technology based on the scientific theory of physics.