Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
- URL: http://arxiv.org/abs/2405.15356v1
- Date: Fri, 24 May 2024 08:46:31 GMT
- Title: Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
- Authors: Beitao Chen, Xinyu Lyu, Lianli Gao, Jingkuan Song, Heng Tao Shen,
- Abstract summary: 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.
- Score: 123.54980913741828
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although 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 that appropriately widens the contrastive logits gap between hallucinatory and targeted ones. However, due to uncontrollable nature of the global visual uncertainty, they struggle to precisely induce the hallucinatory tokens, which severely limits their effectiveness in mitigating hallucinations and may even lead to the generation of undesired hallucinations. To tackle this issue, we conducted the theoretical analysis to promote the effectiveness of contrast decoding. Building on this insight, we introduce a novel optimization strategy named Hallucination-Induced Optimization (HIO). This strategy seeks to amplify the contrast between hallucinatory and targeted tokens relying on a fine-tuned theoretical preference model (i.e., Contrary Bradley-Terry Model), thereby facilitating efficient contrast decoding to alleviate hallucinations in LVLMs. Extensive experimental research demonstrates that our HIO strategy can effectively reduce hallucinations in LVLMs, outperforming state-of-the-art methods across various benchmarks.
Related papers
- ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models [65.12177400764506]
Large language models (LLMs) exhibit hallucinations in long-form question-answering tasks across various domains and wide applications.
Current hallucination detection and mitigation datasets are limited in domains and sizes.
This paper introduces an iterative self-training framework that simultaneously and progressively scales up the hallucination annotation dataset.
arXiv Detail & Related papers (2024-07-05T17:56:38Z) - Mitigating Object Hallucination via Data Augmented Contrastive Tuning [52.43197107069751]
Multimodal Large Language Models (MLLMs) tend to hallucinate factually inaccurate information.
We introduce a contrastive tuning method that can be applied to a pretrained off-the-shelf MLLM for mitigating hallucinations.
arXiv Detail & Related papers (2024-05-28T23:36:00Z) - Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback [48.065569871444275]
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) - Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding [25.489832294197797]
This paper introduces the Instruction Contrastive Decoding (ICD) method, a novel approach designed to reduce hallucinations during LVLM inference.
Our method is inspired by our observation that what we call disturbance instructions significantly exacerbate hallucinations in multimodal fusion modules.
arXiv Detail & Related papers (2024-03-27T16:04:47Z) - IBD: Alleviating Hallucinations in Large Vision-Language Models via
Image-Biased Decoding [37.16880672402059]
Over-reliance on linguistic priors has been identified as a key factor leading to hallucinations.
We propose to alleviate this problem by introducing a novel image-biased decoding technique.
Our method derives the next-token probability distribution by contrasting predictions from a conventional LVLM with those of an image-biased LVLM.
arXiv Detail & Related papers (2024-02-28T16:57:22Z) - Hal-Eval: A Universal and Fine-grained Hallucination Evaluation
Framework for Large Vision Language Models [36.98580310654515]
We introduce a refined taxonomy of hallucinations, featuring a new category: Event Hallucination.
We then utilize advanced LLMs to generate and filter fine grained hallucinatory data consisting of various types of hallucinations.
The proposed benchmark distinctively assesses LVLMs ability to tackle a broad spectrum of hallucinations.
arXiv Detail & Related papers (2024-02-24T05:14:52Z) - Alleviating Hallucinations of Large Language Models through Induced
Hallucinations [67.35512483340837]
Large language models (LLMs) have been observed to generate responses that include inaccurate or fabricated information.
We propose a simple textitInduce-then-Contrast Decoding (ICD) strategy to alleviate hallucinations.
arXiv Detail & Related papers (2023-12-25T12:32:49Z) - HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data [102.56792377624927]
hallucinations inherent in machine-generated data remain under-explored.
We present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm.
Our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA.
arXiv Detail & Related papers (2023-11-22T04:52:58Z)
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.