Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework

Ante Wang; Jinsong Su; Shiyu Liu; Xinyi Wen; Zhibin Lan

arxiv: 2601.22451 · v2 · submitted 2026-01-30 · 💻 cs.CV · cs.AI

Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework

Shiyu Liu , Xinyi Wen , Zhibin Lan , Ante Wang , Jinsong Su This is my paper

Pith reviewed 2026-05-16 10:06 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords object hallucinationlarge vision-language modelsimage captioningself-validation frameworklanguage priorstraining-freehallucination mitigationLVLMs

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The pith

LVLMs cut object hallucinations in image captions by validating existence without language priors.

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

Large vision-language models tend to invent objects in their image descriptions because reliance on language patterns grows stronger with longer generations, raising the chance of hallucinated tokens. The paper demonstrates this pattern through experiments and counters it with a verification process that checks whether mentioned objects actually appear in the image, free from language bias. This verification powers a training-free framework that produces several candidate captions, confirms real objects in them, and then selects or merges the reliable portions. The result is markedly fewer hallucinations on standard tests, including a 65.6 percent gain on the CHAIRI metric for LLaVA-v1.5-7B that beats earlier approaches. Readers should care because the work shows these models can become more accurate by examining their own outputs rather than depending on external corrections.

Core claim

The authors establish that over-reliance on language priors causes inflated probabilities for hallucinated object tokens as generation length increases, and that a Language-Prior-Free Verification step allows LVLMs to assess object existence confidence faithfully. This underpins a novel training-free Self-Validation Framework that samples candidate captions, validates objects within them, and mitigates hallucination through caption selection or aggregation, delivering substantial gains such as a 65.6 percent improvement on the CHAIRI metric with LLaVA-v1.5-7B while surpassing previous state-of-the-art methods.

What carries the argument

Language-Prior-Free Verification, which judges object existence confidence independently of language priors, serves as the foundation for the Self-Validation Framework that processes sampled captions to enable selection or aggregation.

If this is right

Image captioning outputs from LVLMs become more reliable because nonexistent objects are filtered before final selection.
The gains apply across different LVLMs without any retraining or fine-tuning steps.
The approach outperforms prior logit-calibration techniques that also target language priors.
Hallucination rates drop even for longer captions where over-reliance on language patterns is strongest.

Where Pith is reading between the lines

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

The verification technique could be adapted to reduce hallucinations in related tasks such as visual question answering.
Models may possess broader untapped self-correction abilities that can be activated through similar internal checks.
Embedding the verification step directly into the generation process instead of post-sampling could support more efficient real-time use.

Load-bearing premise

The Language-Prior-Free Verification step can accurately judge object existence without reintroducing language priors or requiring any model training.

What would settle it

Applying the self-validation framework to LLaVA models on standard image captioning benchmarks and finding no reduction or an increase in CHAIRI hallucination scores compared to the baseline would falsify the central claim.

read the original abstract

Despite progress in Large Vision Language Models (LVLMs), object hallucination remains a critical issue in image captioning task, where models generate descriptions of non-existent objects, compromising their reliability. Previous work attributes this to LVLMs' over-reliance on language priors and attempts to mitigate it through logits calibration. However, they still lack a thorough analysis of the over-reliance. To gain a deeper understanding of over-reliance, we conduct a series of preliminary experiments, indicating that as the generation length increases, LVLMs' over-reliance on language priors leads to inflated probability of hallucinated object tokens, consequently exacerbating object hallucination. To circumvent this issue, we propose Language-Prior-Free Verification to enable LVLMs to faithfully verify the confidence of object existence. Based on this, we propose a novel training-free Self-Validation Framework to counter the over-reliance trap. It first validates objects' existence in sampled candidate captions and further mitigates object hallucination via caption selection or aggregation. Experiment results demonstrate that our framework mitigates object hallucination significantly in image captioning task (e.g., 65.6% improvement on CHAIRI metric with LLaVA-v1.5-7B), surpassing the previous SOTA methods. This result highlights a novel path towards mitigating hallucination by unlocking the inherent potential within LVLMs themselves.

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.

Desk Editor's Note private letter to a colleague

The self-validation framework gives a training-free route to lower object hallucinations in LVLMs by having the model check its own outputs, but the language-prior-free claim is the part that needs the most checking.

read the letter

The paper's main contribution is a self-validation framework that samples multiple candidate captions from an LVLM, then uses the model itself to verify which objects actually exist in the image through a language-prior-free step, and finally selects or aggregates the most reliable caption. This is motivated by their preliminary experiments showing that as generation length grows, the probability of hallucinated object tokens rises due to increasing over-reliance on language priors. This approach is new because it moves beyond simple logits calibration by introducing an internal verification process that aims to unlock the model's own capabilities without extra training. It performs well in the reported experiments, delivering notable reductions in hallucination metrics like CHAIRI, with a claimed 65.6% improvement on LLaVA-v1.5-7B that surpasses prior state-of-the-art techniques. The training-free nature makes it easy to apply to existing models. However, the soft spot lies in the Language-Prior-Free Verification. The description indicates that the same LVLM is used to check object existence confidence, but without an explicit way to prevent the model from using its language priors—such as through masking or modified conditioning—the verification may still be influenced by learned language statistics. This raises the possibility that the benefits come primarily from selecting among candidates rather than from truly independent assessment. The abstract lacks details on experimental controls, statistical significance, or full methodology, which makes it hard to assess how robust the gains are. This paper would be useful for researchers and practitioners working on vision-language models for tasks requiring accurate object descriptions, such as in automation or accessibility. It deserves a serious referee because the core idea is interesting and the results are promising, even if the paper needs to provide more evidence on the verification mechanism and experimental rigor to fully convince.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a training-free Self-Validation Framework to mitigate object hallucination in LVLMs for image captioning. Preliminary experiments link increased hallucination to over-reliance on language priors as generation length grows. The core innovation is a Language-Prior-Free Verification step that uses the LVLM itself to assess object existence confidence in sampled candidate captions, followed by selection or aggregation to produce the final output. The abstract reports a 65.6% CHAIRI improvement on LLaVA-v1.5-7B that surpasses prior SOTA methods.

Significance. If the central claim holds, the work would be significant for providing a simple, training-free procedure that unlocks self-correction within existing LVLMs rather than relying on external calibration or retraining. This could generalize to other hallucination settings and reduce dependence on language priors without architectural changes.

major comments (2)

[Abstract] Abstract and preliminary experiments: the headline 65.6% CHAIRI reduction is presented without any description of experimental controls, statistical tests, number of runs, or full evaluation protocol, which is load-bearing for the quantitative claim that the framework surpasses prior SOTA.
[Language-Prior-Free Verification] Language-Prior-Free Verification step: no explicit mechanism (masking, logit surgery, auxiliary head, or prompt isolation) is described that demonstrably severs the LVLM's pre-trained language statistics; without such a mechanism the verification scores may remain contaminated by language priors, so the reported gains could arise from caption selection heuristics rather than prior removal.

minor comments (1)

[Preliminary experiments] The preliminary experiments are referenced but no supporting data, tables, or figures are shown, reducing clarity on how over-reliance scales with generation length.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below with honest responses and indicate the revisions we will incorporate.

read point-by-point responses

Referee: [Abstract] Abstract and preliminary experiments: the headline 65.6% CHAIRI reduction is presented without any description of experimental controls, statistical tests, number of runs, or full evaluation protocol, which is load-bearing for the quantitative claim that the framework surpasses prior SOTA.

Authors: We agree that the abstract would be strengthened by briefly noting the evaluation protocol. The full manuscript follows the standard CHAIR benchmark on COCO with identical settings to prior SOTA methods for fair comparison. We will revise the abstract to mention the dataset, metric, and that results are obtained under the same protocol as baselines. We will also expand the preliminary experiments section to explicitly describe controls and report variance across runs. revision: yes
Referee: [Language-Prior-Free Verification] Language-Prior-Free Verification step: no explicit mechanism (masking, logit surgery, auxiliary head, or prompt isolation) is described that demonstrably severs the LVLM's pre-trained language statistics; without such a mechanism the verification scores may remain contaminated by language priors, so the reported gains could arise from caption selection heuristics rather than prior removal.

Authors: The verification step uses a targeted prompt that queries the LVLM for per-object existence confidence directly from the image and candidate object, avoiding full-sentence generation where priors accumulate. This prompt isolation is the core mechanism, though we acknowledge it does not include logit masking or auxiliary components. We will add the exact prompt template and a discussion of its design rationale to the method section so readers can evaluate residual prior influence, and we will clarify that gains are measured against selection-only baselines. revision: yes

Circularity Check

0 steps flagged

No circularity: procedural framework with independent experimental validation

full rationale

The paper presents a training-free Self-Validation Framework built around a Language-Prior-Free Verification step. No equations, fitted parameters, or derivations are defined that reduce to their own inputs by construction. The central performance claims (e.g., CHAIRI improvements) rest on reported experimental results rather than on any self-referential proof, renamed empirical pattern, or load-bearing self-citation chain. Preliminary experiments are used only to motivate the design and do not create a circular dependency in the method itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that over-reliance on language priors is the primary driver of object hallucination and that an internal verification step can isolate it without external supervision.

axioms (1)

domain assumption LVLMs' object hallucination stems primarily from over-reliance on language priors that inflates hallucinated token probabilities with longer generations
This premise is stated as the outcome of the authors' preliminary experiments and motivates the entire framework.

pith-pipeline@v0.9.0 · 5562 in / 1098 out tokens · 52719 ms · 2026-05-16T10:06:40.009357+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Language-Prior-Free Verification ... prompt the LVLMs with instruction x_e: 'Describe any element of the image with only one word or phrase'
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Jensen-Shannon Divergence (JSD) ... between p_θ(yt | v,x, y<t) and p_θ(yt | x, y<t)

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