A Survey on Hallucination in Large Vision-Language Models
- URL: http://arxiv.org/abs/2402.00253v2
- Date: Mon, 6 May 2024 01:10:01 GMT
- Title: A Survey on Hallucination in Large Vision-Language Models
- Authors: Hanchao Liu, Wenyuan Xue, Yifei Chen, Dapeng Chen, Xiutian Zhao, Ke Wang, Liping Hou, Rongjun Li, Wei Peng,
- Abstract summary: Large Vision-Language Models (LVLMs) have attracted growing attention within the AI landscape for its practical implementation potential.
However, hallucination'', or more specifically, the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs.
We dissect LVLM-related hallucinations in an attempt to establish an overview and facilitate future mitigation.
- Score: 18.540878498840435
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent development of Large Vision-Language Models (LVLMs) has attracted growing attention within the AI landscape for its practical implementation potential. However, ``hallucination'', or more specifically, the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs. In this comprehensive survey, we dissect LVLM-related hallucinations in an attempt to establish an overview and facilitate future mitigation. Our scrutiny starts with a clarification of the concept of hallucinations in LVLMs, presenting a variety of hallucination symptoms and highlighting the unique challenges inherent in LVLM hallucinations. Subsequently, we outline the benchmarks and methodologies tailored specifically for evaluating hallucinations unique to LVLMs. Additionally, we delve into an investigation of the root causes of these hallucinations, encompassing insights from the training data and model components. We also critically review existing methods for mitigating hallucinations. The open questions and future directions pertaining to hallucinations within LVLMs are discussed to conclude this survey.
Related papers
- A Survey of Hallucination in Large Visual Language Models [48.794850395309076]
The existence of hallucinations has limited the potential and practical effectiveness of LVLM in various fields.
The structure of LVLMs and main causes of hallucination generation are introduced.
The available hallucination evaluation benchmarks for LVLMs are presented.
arXiv Detail & Related papers (2024-10-20T10:58:58Z) - Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models? [53.89380284760555]
Large vision-language models (LVLMs) produce captions that mention concepts that cannot be found in the image.
These hallucinations erode the trustworthiness of LVLMs and are arguably among the main obstacles to their ubiquitous adoption.
Recent work suggests that addition of grounding objectives -- those that explicitly align image regions or objects to text spans -- reduces the amount of LVLM hallucination.
arXiv Detail & Related papers (2024-06-20T16:56:11Z) - Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models [35.45859414670449]
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) - The Dawn After the Dark: An Empirical Study on Factuality Hallucination
in Large Language Models [134.6697160940223]
hallucination poses great challenge to trustworthy and reliable deployment of large language models.
Three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them.
This work presents a systematic empirical study on LLM hallucination, focused on the the three aspects of hallucination detection, source and mitigation.
arXiv Detail & Related papers (2024-01-06T12:40:45Z) - A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions [40.79317187623401]
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP)
LLMs are prone to hallucination, generating plausible yet nonfactual content.
This phenomenon raises significant concerns over the reliability of LLMs in real-world information retrieval systems.
arXiv Detail & Related papers (2023-11-09T09:25:37Z) - Siren's Song in the AI Ocean: A Survey on Hallucination in Large
Language Models [116.01843550398183]
Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks.
LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
arXiv Detail & Related papers (2023-09-03T16:56:48Z) - Evaluation and Analysis of Hallucination in Large Vision-Language Models [49.19829480199372]
Large Vision-Language Models (LVLMs) have recently achieved remarkable success.
LVLMs are still plagued by the hallucination problem.
Hallucination refers to the information of LVLMs' responses that does not exist in the visual input.
arXiv Detail & Related papers (2023-08-29T08:51:24Z) - Evaluating Object Hallucination in Large Vision-Language Models [122.40337582958453]
This work presents the first systematic study on object hallucination of large vision-language models (LVLMs)
We find that LVLMs tend to generate objects that are inconsistent with the target images in the descriptions.
We propose a polling-based query method called POPE to evaluate the object hallucination.
arXiv Detail & Related papers (2023-05-17T16:34:01Z)
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.