LanP: Rethinking the Impact of Language Priors in Large Vision-Language Models
This addresses the issue of hallucination for real-world adoption of LVLMs, but it is incremental as it focuses on benchmarking rather than solving the problem.
The paper tackles the problem of hallucination in Large Vision-Language Models (LVLMs) by proposing a benchmark called LanP to assess the impact of language priors, revealing that many models, including GPT-4 Turbo, have insufficient language priors with accuracies below 0.5 in scenarios with partially hidden objects.
Large Vision-Language Models (LVLMs) have shown impressive performance in various tasks. However, LVLMs suffer from hallucination, which hinders their adoption in the real world. Existing studies emphasized that the strong language priors of LVLMs can overpower visual information, causing hallucinations. However, the positive role of language priors is the key to a powerful LVLM. If the language priors are too weak, LVLMs will struggle to leverage rich parameter knowledge and instruction understanding abilities to complete tasks in challenging visual scenarios where visual information alone is insufficient. Therefore, we propose a benchmark called LanP to rethink the impact of Language Priors in LVLMs. It is designed to investigate how strong language priors are in current LVLMs. LanP consists of 170 images and 340 corresponding well-designed questions. Extensive experiments on 25 popular LVLMs reveal that many LVLMs' language priors are not strong enough to effectively aid question answering when objects are partially hidden. Many models, including GPT-4 Turbo, exhibit an accuracy below 0.5 in such a scenario.