A Survey of Multimodal Hallucination Evaluation and Detection

• URL: http://arxiv.org/abs/2507.19024v1
• Date: Fri, 25 Jul 2025 07:22:42 GMT
• Title: A Survey of Multimodal Hallucination Evaluation and Detection
• Authors: Zhiyuan Chen, Yuecong Min, Jie Zhang, Bei Yan, Jiahao Wang, Xiaozhen Wang, Shiguang Shan,
• Abstract summary: Multi-modal Large Language Models (MLLMs) have emerged as a powerful paradigm for integrating visual and textual information.<n>These models often suffer from hallucination, producing content that appears plausible but contradicts the input content or established world knowledge.<n>This survey offers an in-depth review of hallucination evaluation benchmarks and detection methods across Image-to-Text (I2T) and Text-to-image (T2I) generation tasks.
• Score: 52.03164192840023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal Large Language Models (MLLMs) have emerged as a powerful paradigm for integrating visual and textual information, supporting a wide range of multi-modal tasks. However, these models often suffer from hallucination, producing content that appears plausible but contradicts the input content or established world knowledge. This survey offers an in-depth review of hallucination evaluation benchmarks and detection methods across Image-to-Text (I2T) and Text-to-image (T2I) generation tasks. Specifically, we first propose a taxonomy of hallucination based on faithfulness and factuality, incorporating the common types of hallucinations observed in practice. Then we provide an overview of existing hallucination evaluation benchmarks for both T2I and I2T tasks, highlighting their construction process, evaluation objectives, and employed metrics. Furthermore, we summarize recent advances in hallucination detection methods, which aims to identify hallucinated content at the instance level and serve as a practical complement of benchmark-based evaluation. Finally, we highlight key limitations in current benchmarks and detection methods, and outline potential directions for future research.

Related papers

• When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding [72.15848305976706]
  Large Multimodal Models (LMMs) have achieved impressive progress in visual perception and reasoning.<n>When confronted with visually ambiguous or non-semantic scene text, they often struggle to accurately spot and understand the content.<n>We propose a training-free semantic hallucination mitigation framework comprising two key components.
  arXiv  Detail & Related papers  (2025-06-05T19:53:19Z)
• A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models [30.037505914306504]
  Vision-Language Models (LVLMs) demonstrate remarkable capabilities in multimodal tasks.<n>LVLMs generate inaccurate visual object-related information based on the query input, potentially leading to misinformation and concerns about safety and reliability.<n>In this paper, we analyze each component of LLaVA-like LVLMs to identify potential sources of error and their impact.
  arXiv  Detail & Related papers  (2025-05-04T01:47:58Z)
• Towards a Systematic Evaluation of Hallucinations in Large-Vision Language Models [57.58426038241812]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable performance in complex multimodal tasks.<n>These models still suffer from hallucinations when required to implicitly recognize or infer diverse visual entities from images.<n>We propose a novel visual question answering (VQA) benchmark that employs contextual reasoning prompts as hallucination attacks.
  arXiv  Detail & Related papers  (2024-12-29T23:56:01Z)
• H-POPE: Hierarchical Polling-based Probing Evaluation of Hallucinations in Large Vision-Language Models [0.0]
  We propose H-POPE, a coarse-to-fine-grained benchmark that assesses hallucinations in object existence and attributes. Our evaluation shows that models are prone to hallucinations on object existence, and even more so on fine-grained attributes.
  arXiv  Detail & Related papers  (2024-11-06T17:55:37Z)
• Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models [13.48296910438554]
  We introduce Reefknot, a comprehensive benchmark targeting relation hallucinations, comprising over 20,000 real-world samples.<n>We provide a systematic definition of relation hallucinations, integrating perceptive and cognitive perspectives, and construct a relation-based corpus using the Visual Genome scene graph dataset.<n>We propose a novel confidence-based mitigation strategy, which reduces the hallucination rate by an average of 9.75% across three datasets, including Reefknot.
  arXiv  Detail & Related papers  (2024-08-18T10:07:02Z)
• VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models [57.43276586087863]
  Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs. Existing benchmarks are often limited in scope, focusing mainly on object hallucinations. We introduce a multi-dimensional benchmark covering objects, attributes, and relations, with challenging images selected based on associative biases.
  arXiv  Detail & Related papers  (2024-04-22T04:49:22Z)
• Quantity Matters: Towards Assessing and Mitigating Number Hallucination in Large Vision-Language Models [57.42800112251644]
  We focus on a specific type of hallucination-number hallucination, referring to models incorrectly identifying the number of certain objects in pictures. We devise a training approach aimed at improving consistency to reduce number hallucinations, which leads to an 8% enhancement in performance over direct finetuning methods.
  arXiv  Detail & Related papers  (2024-03-03T02:31:11Z)
• Unified Hallucination Detection for Multimodal Large Language Models [44.333451078750954]
  Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. We present a novel meta-evaluation benchmark, MHaluBench, meticulously crafted to facilitate the evaluation of advancements in hallucination detection methods. We unveil a novel unified multimodal hallucination detection framework, UNIHD, which leverages a suite of auxiliary tools to validate the occurrence of hallucinations robustly.
  arXiv  Detail & Related papers  (2024-02-05T16:56:11Z)
• Hallucination Augmented Contrastive Learning for Multimodal Large Language Model [53.65682783591723]
  Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However, MLLMs still face a fundamental limitation of hallucinations, where they tend to generate erroneous or fabricated information. In this paper, we address hallucinations in MLLMs from a novel perspective of representation learning.
  arXiv  Detail & Related papers  (2023-12-12T04:05:15Z)

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.