Mitigating Hallucinations In Large Vision-language Models: A Latent Space Steering Approach

Hallucination remains a important situation successful deploying Large Vision-Language Models (LVLMs), arsenic these models often make matter misaligned pinch ocular inputs. Unlike mirage successful LLMs, which arises from linguistic inconsistencies, LVLMs struggle pinch cross-modal discrepancies, starring to inaccurate image descriptions aliases incorrect spatial relationships. These models leverage imagination encoders, specified arsenic CLIP, alongside pretrained matter decoders to representation ocular accusation into language. Despite their beardown capacity successful tasks for illustration image captioning, ocular mobility answering, and aesculapian curen planning, LVLMs stay prone to hallucination, which limits their real-world applicability. The rumor stems from various factors, including statistical biases successful pretraining, an over-reliance connected connection priors, and characteristic learning biases. However, existing investigation often fails to relationship for nan unsocial architecture of LVLMs, treating their mirage mechanisms likewise to those successful LLMs contempt nan chopped domiciled of ocular input processing.

To mitigate mirage successful LVLMs, researchers person explored some training-based and training-free approaches. Training-based solutions attraction connected enhancing exemplary alignment pinch crushed truth done further supervision, but they require extended datasets and computational resources. In contrast, training-free methods, specified arsenic self-feedback correction and auxiliary exemplary integration, person gained fame owed to their efficiency. Some approaches refine nan matter decoding process to trim inconsistencies, but these often neglect to reside mirage from nan ocular encoder. As LVLMs evolve, processing targeted solutions that see ocular and textual components will beryllium important for improving their robustness and reliability successful real-world applications.

Researchers from Stanford University analyse nan mechanisms down hallucinations successful LVLMs, focusing connected nan instability of imagination encoders and their effect connected matter decoders. They present Visual and Textual Intervention (VTI), a test-time method stabilizing imagination features by modifying latent abstraction representations. Unlike accepted smoothing methods, VTI pre-computes translator directions from perturbed images and applies them to caller queries, reducing hallucinations without other training costs. Experimental results show that VTI consistently outperforms baseline approaches crossed aggregate benchmarks, emphasizing nan value of imagination characteristic stableness successful mitigating hallucinations and improving LVLM reliability.

LVLMs comprise a imagination encoder and a matter decoder, wherever unstable imagination features tin lead to hallucinations. Researchers place that perturbations successful imagination embeddings origin inconsistencies successful generated text. To reside this, they propose VTI, which pre-computes unchangeable characteristic shifts utilizing Principal Component Analysis (PCA) connected perturbed image embeddings. These shifts are past applied to caller queries, improving characteristic stableness without further training. VTI besides adjusts matter decoder embeddings to trim hallucinations. Experiments corroborate its effectiveness successful mitigating hallucinations while maintaining computational ratio crossed divers tasks and datasets.

The study evaluates nan effectiveness of VTI successful mitigating hallucinations successful LVLMs. Using 80 COCO image-text pairs, nan method generalizes crossed tasks and datasets. Experiments connected POPE, CHAIR, and MMHAL-Bench show VTI’s superiority complete baseline methods for illustration OPERA and VCD. Results show that ocular involution stabilizes characteristic representations while textual involution enhances image attention. Their operation improves accuracy while maintaining matter richness. Additionally, an ablation study connected α and β confirms their effect connected reducing hallucinations. VTI efficaciously addresses multimodal hallucinations without compromising contented quality.

In conclusion, nan study presents VTI arsenic an effective method to mitigate hallucinations successful LVLMs. Unlike hallucinations successful LLMs, those successful LVLMs stem from misalignments betwixt ocular inputs and textual outputs, often owed to separately pre-trained image encoders and matter decoders. VTI stabilizes imagination features by adjusting latent abstraction representations during inference, requiring nary further training. Experimental results corroborate its superiority complete baseline methods successful reducing hallucinations while maintaining output quality. These findings stress nan value of robust characteristic representation, paving nan measurement for much meticulous and reliable LVLM applications successful real-world settings.

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