HELPD : Mitigating Hallucination of LVLM s by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (2024)

Abstract: Large Vision-Language Models (LVLMs) have shown remarkable performance on many visual-language tasks. However, these models still suffer from multimodal hallucination , which means the generation of objects or content that violates the images. Many existing work detects hallucination by directly judging whether an object exists in an image, overlooking the association between the object and semantics. To address this issue, we propose Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (HELPD). This framework incorporates hallucination feedback at both object and sentence semantic levels. Remarkably, even with a marginal degree of training, this approach can alleviate over 15% of hallucination. Simultaneously, HELPD penalizes the output logits according to the image attention window to avoid being overly affected by generated text. HELPD can be seamlessly integrated with any LVLMs. Our experiments demonstrate that the proposed framework yields favorable results across multiple hallucination benchmarks. It effectively mitigates hallucination for different LVLMs and concurrently improves their text generation quality.

## introduction large language models (llms) @xcite @xcite , guided by human instruction, have demonstrated impressive performance in numerous natural language processing (nlp) tasks @xcite . in light of the success of llms, researchers aspire to integrate the powerful capabilities of llms into multimodal domains, consequently introducing large vision-language models (lvlms) @xcite @xcite @xcite . particularly, describe the content of the image in detail. the image features a squirrel jumping in the air, leaping over snow-covered ground. it is likely that this action was taken to avoid dangerous predators or catch food hidden beneath it. this action takes place during winter, with new snowflakes covering everything around, except for a few spots of grass and trees that can be seen on both sides. this scene happens after the squirrel's jump across an open area, where everything, including roofs, walls, and fences, is covered by thick layers of snow. ## conclusion in this paper, we aim to alleviate hallucinations in large vision-language models (lvlms), and propose the helpd framework, which employs the hierarchical feedback learning for small amounts of training on the model. to enhance attention to the visual modality, we also propose a vision-enhanced penalty decoding strategy. to evaluate the effectiveness of our approach, we conduct evaluations on numerous benchmarks. experimental results demonstrate that our proposed framework effectively mitigates hallucination for different lvlms without impacting sentence length and concurrently improves their text generation quality. future work could focus on a more comprehensive evaluation of hallucination at different granularities.