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arxiv: 2604.05623 · v1 · submitted 2026-04-07 · 💻 cs.CV · cs.CL· cs.MM

Recognition: no theorem link

DetailVerifyBench: A Benchmark for Dense Hallucination Localization in Long Image Captions

Xinran Wang , Yuxuan Zhang , Xiao Zhang , Haolong Yan , Muxi Diao , Songyu Xu , Zhonghao Yan , Hongbing Li
show 2 more authors
Kongming Liang Zhanyu Ma
Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:34 UTC · model grok-4.3

classification 💻 cs.CV cs.CLcs.MM
keywords hallucination localizationimage captioningmultimodal large language modelsbenchmark datasetdense annotationslong-form captionserror detection
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The pith

DetailVerifyBench supplies 1,000 images with token-level hallucination annotations in captions averaging over 200 words.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces DetailVerifyBench to fill the gap left by existing benchmarks that only detect broad inconsistencies in image captions rather than pinpointing exact erroneous words or spans. Current multimodal models generate long, narrative-style captions, so evaluation must move to dense, fine-grained localization across hundreds of tokens and multiple hallucination types. The benchmark uses 1,000 images drawn from five domains, each paired with lengthy human-written captions and exhaustive annotations marking where and what kind of errors occur. This setup matters because reliable detailed captioning requires models to know precisely where they have invented or distorted visual content.

Core claim

The authors construct DetailVerifyBench as a collection of 1,000 high-quality images spanning five distinct domains, each accompanied by captions longer than 200 words on average and equipped with dense, token-level annotations that identify multiple categories of hallucinations, thereby creating the most demanding test currently available for exact localization of errors in long image captions.

What carries the argument

DetailVerifyBench, a dataset of images and densely annotated long captions that supports token-by-token evaluation of hallucination localization.

If this is right

  • Models can now be scored on their ability to name the exact location and type of each error instead of receiving only a pass-fail verdict.
  • Development of localization-aware training methods becomes measurable across a range of caption lengths and visual domains.
  • Comparison of multimodal models gains a shared, granular reference that isolates failures at the word or phrase level.
  • The five-domain coverage makes it possible to check whether localization performance holds outside narrow image categories.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Widespread use of the benchmark would likely push research toward methods that output explicit error locations rather than confidence scores alone.
  • The dataset could serve as a training signal for models that learn to revise their own captions by first identifying hallucinated segments.
  • Similar dense-annotation approaches may prove useful for evaluating long-form outputs in related tasks such as video narration or document description.

Load-bearing premise

The human annotations correctly and comprehensively mark every hallucination without systematic bias or omission.

What would settle it

Independent re-annotation of a random subset of the captions by separate annotators produces substantially different hallucination spans or types.

Figures

Figures reproduced from arXiv: 2604.05623 by Haolong Yan, Hongbing Li, Kongming Liang, Muxi Diao, Songyu Xu, Xiao Zhang, Xinran Wang, Yuxuan Zhang, Zhanyu Ma, Zhonghao Yan.

Figure 1
Figure 1. Figure 1: The difference between the task of hallucination detection and hallucination localization. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The pipeline for building the DetailVerifyBench. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of 10 hallucination dimensions across [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) Comparison of injection methods; (b) Hallucination counts & [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of a GUI image example with real [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
read the original abstract

Accurately detecting and localizing hallucinations is a critical task for ensuring high reliability of image captions. In the era of Multimodal Large Language Models (MLLMs), captions have evolved from brief sentences into comprehensive narratives, often spanning hundreds of words. This shift exponentially increases the challenge: models must now pinpoint specific erroneous spans or words within extensive contexts, rather than merely flag response-level inconsistencies. However, existing benchmarks lack the fine granularity and domain diversity required to evaluate this capability. To bridge this gap, we introduce DetailVerifyBench, a rigorous benchmark comprising 1,000 high-quality images across five distinct domains. With an average caption length of over 200 words and dense, token-level annotations of multiple hallucination types, it stands as the most challenging benchmark for precise hallucination localization in the field of long image captioning to date. Our benchmark is available at https://zyx-hhnkh.github.io/DetailVerifyBench/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces DetailVerifyBench, a benchmark consisting of 1,000 high-quality images across five domains, with long captions (average length >200 words) and dense token-level annotations for multiple hallucination types in MLLM-generated image captions. It positions the resource as the most challenging benchmark to date for precise hallucination localization in long-form captioning and provides a public link for access.

Significance. If the annotations can be validated as accurate and consistent, the benchmark would address a clear gap in existing resources by enabling fine-grained evaluation of hallucination localization rather than coarse response-level detection. The scale, domain diversity, and public release represent strengths that could support reproducible progress in MLLM reliability research.

major comments (2)
  1. [Abstract] Abstract: The claim that DetailVerifyBench 'stands as the most challenging benchmark for precise hallucination localization... to date' rests on assertions of scale, caption length, annotation density, and domain diversity, yet the manuscript provides no details on annotation methodology, inter-annotator agreement scores, annotation guidelines, or any held-out validation subset. This information is load-bearing for the central claim of rigor and superiority.
  2. [Abstract] The weakest assumption underlying the benchmark's utility is that the human annotations are accurate, comprehensive, and free of systematic bias or omissions. Without reported agreement metrics or quality-control procedures, inconsistencies in localizing erroneous spans within >200-word narratives could undermine comparative difficulty claims and downstream model evaluations.

Simulated Author's Rebuttal

2 responses · 1 unresolved

Thank you for the constructive feedback on our manuscript introducing DetailVerifyBench. We have carefully considered the referee's concerns regarding the documentation of the annotation process and the substantiation of our claims. We agree that additional details are necessary to fully support the benchmark's positioning and will incorporate them in the revised version. Our point-by-point responses follow.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that DetailVerifyBench 'stands as the most challenging benchmark for precise hallucination localization... to date' rests on assertions of scale, caption length, annotation density, and domain diversity, yet the manuscript provides no details on annotation methodology, inter-annotator agreement scores, annotation guidelines, or any held-out validation subset. This information is load-bearing for the central claim of rigor and superiority.

    Authors: We agree that the current version of the manuscript lacks sufficient details on the annotation methodology to fully substantiate the claim. The positioning as the most challenging benchmark is based on the described attributes: 1,000 images across five domains, average caption lengths exceeding 200 words, and dense token-level annotations for multiple hallucination types. These features differentiate it from existing benchmarks. In the revised manuscript, we will expand the abstract and add a dedicated methods section describing the annotation guidelines, process, inter-annotator agreement where measured, and validation procedures. This will provide the load-bearing information requested. revision: yes

  2. Referee: [Abstract] The weakest assumption underlying the benchmark's utility is that the human annotations are accurate, comprehensive, and free of systematic bias or omissions. Without reported agreement metrics or quality-control procedures, inconsistencies in localizing erroneous spans within >200-word narratives could undermine comparative difficulty claims and downstream model evaluations.

    Authors: We acknowledge this as a valid concern and the importance of transparent quality assurance for long-form annotations. The annotations in DetailVerifyBench were created with the goal of high accuracy and comprehensiveness using structured guidelines to identify hallucinations at a fine-grained level. The revised version will include a thorough description of the quality-control procedures implemented to reduce bias and omissions. Additionally, we will report any inter-annotator agreement metrics or other validation steps performed. These additions should alleviate concerns about potential inconsistencies affecting evaluations. revision: partial

standing simulated objections not resolved
  • Specific inter-annotator agreement scores and detailed validation subset results, which were not computed or documented in the original benchmark creation process.

Circularity Check

0 steps flagged

No circularity: benchmark introduction is self-contained resource creation

full rationale

The paper presents DetailVerifyBench as an externally constructed dataset (1,000 images across five domains, >200-word captions, dense token-level hallucination annotations) without any derivation chain, equations, fitted parameters, or predictions that reduce to self-defined inputs. No self-citation load-bearing steps, uniqueness theorems, or ansatzes appear in the provided text; the 'most challenging' claim rests on descriptive scale and granularity rather than reducing by construction to prior author work or fitted quantities. This matches the default expectation of no significant circularity for benchmark papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the assumption that the created annotations and image selection constitute a valid and challenging test, but the abstract introduces no explicit free parameters, mathematical axioms, or new invented entities.

pith-pipeline@v0.9.0 · 5495 in / 1028 out tokens · 64548 ms · 2026-05-10T18:34:55.156835+00:00 · methodology

discussion (0)

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