Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs
- URL: http://arxiv.org/abs/2411.19187v2
- Date: Wed, 19 Feb 2025 05:44:50 GMT
- Title: Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs
- Authors: Anirudh Phukan, Divyansh, Harshit Kumar Morj, Vaishnavi, Apoorv Saxena, Koustava Goswami,
- Abstract summary: We introduce ContextualLens, a refined method that leverages contextual token embeddings from middle layers of LMMs.
This approach significantly improves hallucination detection and grounding across diverse categories, including actions and OCR.
Our contributions pave the way for more reliable and interpretable multimodal models.
- Score: 3.8318712731382054
- License:
- Abstract: The rapid development of Large Multimodal Models (LMMs) has significantly advanced multimodal understanding by harnessing the language abilities of Large Language Models (LLMs) and integrating modality-specific encoders. However, LMMs are plagued by hallucinations that limit their reliability and adoption. While traditional methods to detect and mitigate these hallucinations often involve costly training or rely heavily on external models, recent approaches utilizing internal model features present a promising alternative. In this paper, we critically assess the limitations of the state-of-the-art training-free technique, the logit lens, in handling generalized visual hallucinations. We introduce ContextualLens, a refined method that leverages contextual token embeddings from middle layers of LMMs. This approach significantly improves hallucination detection and grounding across diverse categories, including actions and OCR, while also excelling in tasks requiring contextual understanding, such as spatial relations and attribute comparison. Our novel grounding technique yields highly precise bounding boxes, facilitating a transition from Zero-Shot Object Segmentation to Grounded Visual Question Answering. Our contributions pave the way for more reliable and interpretable multimodal models.
Related papers
- 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) - RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks.
Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs.
In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
arXiv Detail & Related papers (2024-10-18T03:45:19Z) - CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models [51.70129969269271]
We introduce a novel contrastive-based decoding method, COuntering DEscription Contrastive Decoding (CODE)
Our method significantly reduces hallucinations and improves cross-modal consistency across various benchmarks and cutting-edge LMMs.
arXiv Detail & Related papers (2024-06-04T03:04:21Z) - What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models [50.97705264224828]
We propose Counterfactual Inception, a novel method that implants counterfactual thinking into Large Multi-modal Models.
We aim for the models to engage with and generate responses that span a wider contextual scene understanding.
Comprehensive analyses across various LMMs, including both open-source and proprietary models, corroborate that counterfactual thinking significantly reduces hallucination.
arXiv Detail & Related papers (2024-03-20T11:27:20Z) - Incorporating Visual Experts to Resolve the Information Loss in
Multimodal Large Language Models [121.83413400686139]
This paper proposes to improve the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism.
We introduce a novel method that incorporates multi-task encoders and visual tools into the existing MLLMs training and inference pipeline.
arXiv Detail & Related papers (2024-01-06T02:02:34Z) - Sparsity-Guided Holistic Explanation for LLMs with Interpretable
Inference-Time Intervention [53.896974148579346]
Large Language Models (LLMs) have achieved unprecedented breakthroughs in various natural language processing domains.
The enigmatic black-box'' nature of LLMs remains a significant challenge for interpretability, hampering transparent and accountable applications.
We propose a novel methodology anchored in sparsity-guided techniques, aiming to provide a holistic interpretation of LLMs.
arXiv Detail & Related papers (2023-12-22T19:55:58Z) - Zero-Resource Hallucination Prevention for Large Language Models [45.4155729393135]
"Hallucination" refers to instances where large language models (LLMs) generate factually inaccurate or ungrounded information.
We introduce a novel pre-language self-evaluation technique, referred to as SELF-FAMILIARITY, which focuses on evaluating the model's familiarity with the concepts present in the input instruction.
We validate SELF-FAMILIARITY across four different large language models, demonstrating consistently superior performance compared to existing techniques.
arXiv Detail & Related papers (2023-09-06T01:57:36Z)
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.