Large Vision-Language Models (LVLMs) have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs. Despite these advancements achieved, LVLMs still suffer from the hallucination problem, e.g., they tend to produce content that does not exist in the input images. Our investigation suggests that such hallucinations often stem from the deficiencies in fine-grained comprehension on the visual aspect, particularly when visual scenes exhibit appearance or semantic similarities (e.g., bicycle vs. motorcycles, baseball bat vs. baseball). In this work, we show such hallucination is naturally mitigated via a novel method called visual evidence prompting, utilizing small visual models to complement the LVLMs. While traditional visual models are not adept at interacting with humans, they excel at perceiving the fine-grained image contents. By symbolizing the professional outputs of domain-expert models as prompts, the LVLM generalists are able to refer to these evidences as visual knowledge to generate more precise answers. Detailed analysis shows that visual evidence enables models to adjust and rectify the attribution and attention on the images, reducing visual confusion by suppressing false activation while enhancing correct ones. Extensive experiments and in-depth analysis demonstrate the effectiveness of our method. We hope our straightforward but insightful work enhances the comprehension of hallucination in LVLMs and offers valuable perspectives on addressing such challenges.

Due to the immense resource demands and the involved complex techniques, it is still challenging for successfully pre-training a large language models (LLMs) with state-of-the-art performance. In this paper, we explore the key bottlenecks and designs during pre-training, and make the following contributions: (1) a comprehensive investigation into the factors contributing to training instability; (2) a robust optimization approach designed to mitigate training instability effectively; (3) an elaborate data pipeline that integrates data synthesis, data curriculum, and data selection. By integrating the above techniques, we create a rather low-cost training recipe and use it to pre-train YuLan-Mini, a fully-open base model with 2.4B parameters on 1.08T tokens. Remarkably, YuLan-Mini achieves top-tier performance among models of similar parameter scale, with comparable performance to industry-leading models that require significantly more data. To facilitate reproduction, we release the full details of training recipe and data composition. Project details can be accessed at the following link: https://anonymous.4open.science/r/YuLan-Mini/README.md.