Reducing Hallucinations in Vision-Language Models via Latent Space Steering
- URL: http://arxiv.org/abs/2410.15778v2
- Date: Tue, 22 Oct 2024 05:01:28 GMT
- Title: Reducing Hallucinations in Vision-Language Models via Latent Space Steering
- Authors: Sheng Liu, Haotian Ye, Lei Xing, James Zou,
- Abstract summary: Hallucination poses a challenge to the deployment of large vision-language models (LVLMs) in applications.
We introduce Visual and Textual Intervention (VTI), a novel technique designed to reduce hallucinations by steering latent space representations during inference to enhance the stability of vision features.
- Score: 34.1755878632361
- License:
- Abstract: Hallucination poses a challenge to the deployment of large vision-language models (LVLMs) in applications. Unlike in large language models (LLMs), hallucination in LVLMs often arises from misalignments between visual inputs and textual outputs. This paper investigates the underlying mechanisms of hallucination, focusing on the unique structure of LVLMs that distinguishes them from large language models (LLMs). We identify that hallucinations often arise from the sensitivity of text decoders to vision inputs, a natural phenomenon when image encoders and text decoders are pre-trained separately. Inspired by this, we introduce Visual and Textual Intervention (VTI), a novel technique designed to reduce hallucinations by steering latent space representations during inference to enhance the stability of vision features. As a task-agnostic test-time intervention, VTI can be easily applied to any problem without additional cost. Extensive experiments demonstrate that it can effectively reduce hallucinations and outperform baseline methods across multiple metrics, highlighting the critical role of vision feature stability in LVLMs.
Related papers
- Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models [66.71616369573715]
Large Vision-Language Models (LVLMs) are prone to generating hallucinatory text responses that do not align with the given visual input.
We introduce self-correcting Decoding with Generative Feedback (DeGF), a novel training-free algorithm that incorporates feedback from text-to-image generative models into the decoding process.
arXiv Detail & Related papers (2025-02-10T03:43:55Z) - Mitigating Hallucination for Large Vision Language Model by Inter-Modality Correlation Calibration Decoding [66.06337890279839]
Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding for downstream multi-modal tasks.
LVLMs still suffer from generating hallucinations in complex generation tasks, leading to inconsistencies between visual inputs and generated content.
We propose an Inter-Modality Correlation Decoding (IMCCD) method to mitigate hallucinations in LVLMs in a training-free manner.
arXiv Detail & Related papers (2025-01-03T17:56:28Z) - Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence [69.86946427928511]
We investigate the internal mechanisms driving hallucination in large vision-language models (LVLMs)
We introduce Vision-aware Head Divergence (VHD), a metric that quantifies the sensitivity of attention head outputs to visual context.
We propose Vision-aware Head Reinforcement (VHR), a training-free approach to mitigate hallucination by enhancing the role of vision-aware attention heads.
arXiv Detail & Related papers (2024-12-18T15:29:30Z) - Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning [151.4060202671114]
multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing vision-language tasks.
This paper introduces a novel bottom-up reasoning framework to address hallucinations in MLLMs.
Our framework systematically addresses potential issues in both visual and textual inputs by verifying and integrating perception-level information with cognition-level commonsense knowledge.
arXiv Detail & Related papers (2024-12-15T09:10:46Z) - VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive Decoding [38.23310445372371]
Large Vision-Language Models (LVLMs) have demonstrated outstanding performance in multimodal task reasoning.
We propose a novel hallucination-mitigation method from the visual encoding perspective: textbfVisutextbfal textbfLayer Fustextbfion Contrastive textbfDecoding (VaLiD)
arXiv Detail & Related papers (2024-11-24T13:42:02Z) - CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs [74.36850397755572]
CATCH addresses issues related to visual defects that cause diminished fine-grained feature perception and cumulative hallucinations in open-ended scenarios.
It is applicable to various visual question-answering tasks without requiring any specific data or prior knowledge, and generalizes robustly to new tasks without additional training.
arXiv Detail & Related papers (2024-11-19T18:27:31Z) - From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models [15.401221354325672]
Hallucinations in large vision models (LVLMs) are a significant challenge, i.e., generating objects that are not presented in the visual input.
Recent studies often attribute hallucinations to a lack of understanding of visual input, yet ignore a more fundamental issue: the model's inability to extract or decouple visual features.
In this paper, we revisit the hallucinations in LVLMs from an architectural perspective, investigating whether the primary cause lies in the visual encoder (feature extraction) or the modal alignment module (feature decoupling)
arXiv Detail & Related papers (2024-10-09T11:46:32Z) - Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization [123.54980913741828]
Large Visual Language Models (LVLMs) have demonstrated exceptional abilities in understanding multimodal data.
They invariably suffer from hallucinations, leading to a disconnect between the generated text and the corresponding images.
Almost all current visual contrastive decoding methods attempt to mitigate these hallucinations by introducing visual uncertainty information.
However, they struggle to precisely induce the hallucinatory tokens, which severely limits their effectiveness in mitigating hallucinations.
arXiv Detail & Related papers (2024-05-24T08:46:31Z) - Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback [40.930238150365795]
We propose detecting and mitigating hallucinations in Large Vision Language Models (LVLMs) via fine-grained AI feedback.
We generate a small-size hallucination annotation dataset by proprietary models.
Then, we propose a detect-then-rewrite pipeline to automatically construct preference dataset for training hallucination mitigating model.
arXiv Detail & Related papers (2024-04-22T14:46:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.