Despite significant advancements in large visual-language models (LVLMs), hallucinations remain a major bottleneck in their practical applications. One key factor contributing to hallucinations is the over-reliance on language priors during the autoregressive text generation process. Visual Contrastive Decoding (VCD), a popular technique for mitigating hallucinations, perturbs the visual input and compares the perturbed output with the original. However, it often overlooks the gradual attenuation of visual information within the decoder, limiting the model's ability to generate text based on actual visual content. We propose a novel, training-free method—Visual-Enhanced Contrastive Decoding (VECD)—which addresses this issue by amplifying visual information within the decoder, thereby reducing hallucinations caused by excessive reliance on language priors. VECD dynamically selects later layers for visual injection, while retaining only essential visual tokens in early layers. This approach enhances the generation process by adaptively balancing visual and language priors. By comparing outputs with and without visual amplification, we derive a refined probability distribution for the next token. Moreover, we improve the beam search algorithm by introducing a visually guided token selection strategy, enabling the generation of text that aligns more closely with the image content. Our extensive experiments show that VECD significantly reduces hallucinations and improves the quality of generated text, demonstrating its effectiveness as a practical solution.

Data-driven pre-trained language models typically perform shortcut learning wherein they rely on the spurious correlations between the data and the ground truth. This reliance can undermine the robustness and generalization of the model. To address this issue, data augmentation emerges as a promising solution. By integrating anti-shortcut data to the training set, the models’ shortcut-induced biases can be mitigated. However, existing methods encounter three challenges: 1) Manual definition of shortcuts is tailored to particular datasets, restricting generalization. 2) The inherent confirmation bias during model training hampers the effectiveness of data augmentation. 3) Insufficient exploration of the relationship between the model performance and the augmented data quantity may result in excessive data consumption. To tackle these challenges, we propose a method of Smart Data Augmentation based on Large Language Models (SAug-LLM). It leverages the LLMs to autonomously identify shortcuts and generate their anti-shortcut counterparts. In addition, the dual validation is employed to mitigate the confirmation bias during the model retraining. Furthermore, the data augmentation process is optimized to effectively rectify model biases while minimizing data consumption. We validate the effectiveness and generalization of our method through extensive experiments across various natural language processing tasks, demonstrating an average performance improvement of 5.61%.