Large vision-language models (LVLMs) are revolutionizing how AI interacts with the world, allowing them to understand images and text together. However, these models sometimes suffer from “hallucinations,” generating responses that don't match the image because they over-rely on text. Imagine an LVLM describing a sunny beach scene when shown a picture of a snowy mountain! This happens due to a phenomenon called language bias, where the model prioritizes textual cues, even when they contradict visual information. Researchers are tackling this challenge head-on. A new framework called LACING (MuLtimodal DuAl-attention MeChanIsm aNd Soft-Image Guidance) reduces language bias from two angles. First, it uses a dual-attention mechanism, essentially allowing the model to process images and text separately, preventing the text from overwhelming the visual data. Second, it introduces a “soft visual prompt” during training, forcing the model to pay more attention to images even in scenarios designed to favor text. This technique subtly guides the model towards a more balanced understanding of both modalities. Experiments show LACING significantly reduces hallucinations and improves visual comprehension without requiring additional training data or resources. For example, it drastically reduces the instances where an LVLM misidentifies objects in an image, a common problem caused by language bias. This is a big step towards creating AI that truly “sees” and interprets the world around it accurately, leading to more reliable and robust vision-language models in applications like self-driving cars, medical diagnoses, and assistive technologies. While challenges remain, research like this helps us build AI that integrates information from different sources more effectively and avoids the pitfalls of relying too heavily on any single modality.