Asking Questions to Alleviate Object Hallucination in Large Vision-Language Models

Large vision language models (LVLMs) have achieved rapid development. However, just like large language models (LLMs), LVLMs face the critical challenge of hallucination, which refers to the phenomenon that the generation text containing references or descriptions of the input image is incorrect or inconsistent. The causes of hallucinations are complex and therefore difficult to avoid directly during the generation process. To alleviate hallucinations, existing studies mainly employ an instruction-tuning approach that requires model retraining with specific data. Other methods use decoding constraints to penalize specific tokens during the decoding process. These will incur expensive annotation costs and computation burden. In this paper, we propose a framework named AQAH to alleviate hallucinations without relying on manual data and large-scale parameter tuning. AQAH compares multiple generated samples to locate the hallucination factors, and then asks questions about the uncertain information. Finally, the answers to the questions are used to add auxiliary information to the prompt to correct the hallucination of LVLMs during regeneration. To facilitate this process, we constructed an automatic process that involves the training of a small model for question generation, and the agent collaboration framework including the small question generation model and large question answering foundation model. Since AQAH does not directly constrain the decoding, it will not cause a significant degradation in inference efficiency, nor force LVLMs to suffer the notorious problem of shortened text generation length. We experimentally demonstrate the effectiveness of AQAH in hallucination alleviation through the proposed “active questioning & answer verification” paradigm in various multimodal tasks such as captioning and visual question answering. Beyond the promising performance and fewer training/inference time costs against other hallucination reduction methods, our method is highly interpretable and flexible, showing great potential in improving LVLMs by exploiting small-scale models. The code is available at https://github.com/bcxbg/AQAH.