HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning

• URL: http://arxiv.org/abs/2603.15253v2
• Date: Tue, 24 Mar 2026 11:48:00 GMT
• Title: HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning
• Abstract summary: Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text.<n>HalDec-Bench is a benchmark designed to evaluate hallucination detectors in a principled and interpretable manner.
• Score: 27.133240420463807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text by identifying errors in captions that misrepresent the image. Beyond evaluation, effective hallucination detection is also essential for curating high-quality image-caption pairs used to train VLMs. However, the generalizability of VLMs as hallucination detectors across different captioning models and hallucination types remains unclear due to the lack of a comprehensive benchmark. In this work, we introduce HalDec-Bench, a benchmark designed to evaluate hallucination detectors in a principled and interpretable manner. HalDec-Bench contains captions generated by diverse VLMs together with human annotations indicating the presence of hallucinations, detailed hallucination-type categories, and segment-level labels. The benchmark provides tasks with a wide range of difficulty levels and reveals performance differences across models that are not visible in existing multimodal reasoning or alignment benchmarks. Our analysis further uncovers two key findings. First, detectors tend to recognize sentences appearing at the beginning of a response as correct, regardless of their actual correctness. Second, our experiments suggest that dataset noise can be substantially reduced by using strong VLMs as filters while employing recent VLMs as caption generators. Our project page is available at https://dahlian00.github.io/HalDec-Bench-Page/.

Related papers

• When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs [54.411658510110215]
  We propose HalluScope to better understand the extent to which different factors induce hallucinations.<n>HalluVL-DPO is a framework for fine-tuning off-the-shelf LVLMs towards more visually grounded responses.
  arXiv  Detail & Related papers  (2026-04-23T17:54:36Z)
• When Images Speak Louder: Mitigating Language Bias-induced Hallucinations in VLMs through Cross-Modal Guidance [36.230615314462426]
  We analyze how language bias contributes to hallucinations and then introduce Cross-Modal Guidance(CMG)<n>CMG addresses hallucinations by leveraging the difference between the output of the original model and the one with degraded visual-language attention.<n>We show CMG can improve different VLM's performance on hallucination-specific benchmarks and generalize effectively.
  arXiv  Detail & Related papers  (2025-10-12T06:17:13Z)
• Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization [55.543583937522804]
  Multimodal Large Language Models (MLLMs) emerge as a unified interface to address a multitude of tasks.<n>Despite showcasing state-of-the-art results in many benchmarks, a long-standing issue is the tendency of MLLMs to hallucinate.<n>In this paper, we address the problem of hallucinations as an alignment problem, seeking to steer the MLLM so that it prefers generating content without hallucinations.
  arXiv  Detail & Related papers  (2025-08-27T18:02:04Z)
• SHALE: A Scalable Benchmark for Fine-grained Hallucination Evaluation in LVLMs [52.03164192840023]
  Large Vision-Language Models (LVLMs) still suffer from hallucinations, i.e., generating content inconsistent with input or established world knowledge.<n>We propose an automated data construction pipeline that produces scalable, controllable, and diverse evaluation data.<n>We construct SHALE, a benchmark designed to assess both faithfulness and factuality hallucinations.
  arXiv  Detail & Related papers  (2025-08-13T07:58:01Z)
• What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models? [95.46087552542998]
  This paper introduces the Hallucination searching-based Object Probing Evaluation benchmark.<n>It aims to generate the most misleading distractors that can trigger hallucination in Large Vision-Language Models.<n> Experimental results show that HOPE leads to a precision drop of at least 9% and up to 23% across various state-of-the-art LVLMs.
  arXiv  Detail & Related papers  (2025-08-03T03:11:48Z)
• Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models [5.545334160894197]
  Vision-Language Models (VLMs) are becoming increasingly popular in the medical domain, bridging the gap between medical images and clinical language.<n>However, hallucination--the tendency to generate descriptions that are inconsistent with the visual content--remains a significant issue in VLM.<n>To facilitate VLM research on gastrointestinal (GI) image analysis and study hallucination, we curate a multimodal image-text GI dataset.<n>This dataset is created using a two-stage pipeline: first, descriptive medical reports of Kvasir-v2 images are generated using ChatGPT, which introduces some hallucinated or
  arXiv  Detail & Related papers  (2025-05-11T14:54:11Z)
• HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding [36.360171373963716]
  Large Vision-Language Models (LVLMs) have shown remarkable performance on many visual-language tasks. These models still suffer from multimodal hallucination, which means the generation of objects or content that violates the images. We propose Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (HELPD) to address this issue.
  arXiv  Detail & Related papers  (2024-09-30T15:52:05Z)
• Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs [54.50483041708911]
  Hallu-PI is the first benchmark designed to evaluate hallucination in MLLMs within Perturbed Inputs. Hallu-PI consists of seven perturbed scenarios, containing 1,260 perturbed images from 11 object types. Our research reveals a severe bias in MLLMs' ability to handle different types of hallucinations.
  arXiv  Detail & Related papers  (2024-08-02T16:07:15Z)
• Do More Details Always Introduce More Hallucinations in LVLM-based Image Captioning? [29.237078890377514]
  Large Vision-Language Models (LVLMs) excel in integrating visual and linguistic contexts to produce detailed content. Using LVLMs to generate descriptions often faces the challenge of object hallucination (OH), where the output text misrepresents actual objects in the input image. This paper proposes a novel decoding strategy, Differentiated Beam Decoding (DBD), along with a reliable new set of evaluation metrics.
  arXiv  Detail & Related papers  (2024-06-18T14:33:56Z)
• AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models [91.78328878860003]
  Large vision-language models (LVLMs) are prone to hallucinations. benchmarks often rely on hand-crafted corner cases whose failure patterns may not generalize well. We develop AutoHallusion, the first automated benchmark generation approach.
  arXiv  Detail & Related papers  (2024-06-16T11:44:43Z)
• MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification [1.3654846342364308]
  We introduce MetaToken, a lightweight binary classifier to detect hallucinations on the token-level at negligible cost.<n>Based on a statistical analysis, we reveal key factors of hallucinations in Large Vision Language Models.<n>We evaluate our method on four state-of-the-art LVLMs demonstrating the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-05-29T15:28:42Z)
• 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.