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REVIEW 4 major objections 5 minor 53 references

LPOI: Listwise Preference Optimization for Vision Language Models

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Training a vision-language model to rank progressively masked images by object visibility lowers hallucination rates below pairwise preference methods, the paper claims, and needs no extra annotations.

desk verdict Genuinely new VLM preference optimization with plausible empirical gains; the core monotonicity assumption needs a sanity check, but this deserves peer review. read the letter →

arxiv 2505.21061 v1 pith:LIAPXWD2 submitted 2025-05-27 cs.CV cs.AI

Fatemeh Pesaran Zadeh , Yoojin Oh , Gunhee Kim This is my paper
classification cs.CVcs.AI
keywords vision-languagemodelshallucinationreductionlistwisepreferenceoptimizationobjectmaskinghardnegativesamplingdirectvisualgroundingmultimodalalignment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that a vision-language model hallucinates less when it is trained to order images by how clearly the object named in its answer is visible, rather than merely to prefer one image over another. LPOI creates that order automatically: it detects and masks a critical object, then interpolates the masked fraction from the full image to a fully masked hard negative, producing a ranked list at no extra annotation cost. A listwise preference loss then pushes the model's likelihood of the chosen text to fall as visibility falls. Experiments on MMHalBench, AMBER, and Object HalBench report lower hallucination rates and better scores than DPO, mDPO, and other preference-optimization baselines across three base VLMs. If the monotonicity assumption behind the rank is accepted, the result is that dense visual-ordering supervision can serve as a practical antidote to visual grounding failures.

What carries the argument

The load-bearing mechanism is the automatically generated ranked image list together with the listwise loss in Eq. (1). A zero-shot open-vocabulary object detector finds an object named or implied by the chosen answer; its bounding box is progressively masked from the image edge inward, and the masked region is circled so the model attends to it. A filtering step keeps the list only when the fully masked image makes a reference VLM hallucinate. The listwise loss is the negative log-likelihood of the desired ordering of normalized log-likelihood scores, so the model is trained to make the chosen text more probable exactly when the object is more visible. This machinery carries the argument because it converts standard pairwise preference data into dense, graded visual supervision without listwise human annotations.

What would settle it

On a held-out set, compute the trained model's log-likelihood of the chosen answer for the $L$ progressively masked images and have annotators judge whether that answer is still true at each mask level; if likelihood does not decrease monotonically with masking on a substantial share of items, or if the answer is already false on the original image, the listwise training signal is enforcing a false premise. A complementary control is to train on the same masked images as unordered pairs; if the hallucination gains survive, the ranking order is not the active ingredient.

Watch

Extended reading notes

Core claim

LPOI's central claim is that a VLM can be taught to stop hallucinating a mentioned object by optimizing the model to rank images by that object's visibility. The paper claims this is the first listwise preference optimization applied to vision-language models. Given an image, a question, a chosen answer, and a rejected answer, the method detects the critical object, builds a hard negative by masking it, and creates $L$ images whose masks cover $\frac{k-1}{L-1}\times100\%$ of the bounding box for $k=1,\dots,L$, with a visual prompt on the masked region. The training objective is $L_{\mathrm{total}}=L_{\mathrm{DPO}}+L_{\mathrm{Anchor}}+L_{\mathrm{Listwise}}$, where the listwise term is the negative log-likelihood of the permutation of scores $S_k=\beta\log\frac{\pi_\theta(w\mid x_k,q)}{\pi_{\mathrm{ref}}(w\mid x_k,q)}$, pushing $S_1>S_2>\cdots>S_L$. The paper reports that this ordering signal lowers hallucination rates relative to pairwise methods on MMHalBench, AMBER, and Object HalBench across three base models, and that human annotators prefer its responses over mDPO- and DPO-trained ones.

Load-bearing premise

The load-bearing premise is that for every generated list the original chosen answer stays the correct response and the model's likelihood of producing it should fall smoothly as more of the object is covered, with no independent check that this holds at intermediate or full masking.

Editorial extensions

If this is right

  • On the three benchmarks, LPOI consistently lowers hallucination metrics relative to DPO and mDPO across LLaVA-v1.5-7B, LLaVA-v1.5-13B, and Idefics2-8B.
  • Under a fixed 20-hour GPU budget on Idefics2-8B, LPOI with list size 5 still outperforms DPO and mDPO on MMHalBench score and hallucination rate.
  • Larger list sizes improve hallucination metrics on Object HalBench, indicating that the graded list itself, not just a single hard negative, contributes to the gain.
  • Removing the text DPO loss or the anchor loss degrades performance, so the listwise image signal complements rather than replaces textual preference learning.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the monotonicity link between object visibility and answer likelihood holds broadly, the same interpolation idea should transfer to other evidence-damaging transforms such as blur, occlusion by other objects, or lighting changes; the paper's mechanism predicts each would reduce hallucination if applied as a ranked list.
  • Because the ranked list is generated from a single chosen text, LPOI implicitly teaches the model to modulate its confidence with visible evidence, which could generalize to calibration under partial occlusion beyond the three hallucination benchmarks, though that transfer is not established here.
  • A control the paper does not run would train on the same masked images as unordered pairs; if the hallucination gains persist without the ordering, the active ingredient would be extra masked images rather than listwise ranking.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes LPOI, a listwise preference optimization method for vision-language models (VLMs) aimed at reducing object hallucination. The method identifies a critical object in the image, masks it progressively, and interpolates between the original and fully masked images to build a ranked list of images. The model is trained with a listwise loss (Eq. 1) so that the normalized likelihood of the chosen text decreases as the object is more heavily masked, in addition to the standard DPO and an anchor loss. Empirical evaluation on MMHalBench, AMBER, and Object HalBench across three backbones (LLaVA-v1.5-7B/13B, Idefics2-8B) reports consistent hallucination reduction compared to DPO, mDPO, and several published baselines, with additional ablations, a human evaluation, and a same-budget comparison.

Significance. If the reported gains are robust, LPOI is a meaningful contribution: it is the first listwise preference optimization for VLMs, it requires no additional annotations beyond standard pairwise preference data, and it shows consistent improvements across multiple models and benchmarks. The paper includes useful strengths: code release, several ablations (list size, visual prompting, loss components, verification module), a human evaluation with agreement scores, and a same-GPU-budget comparison. The central idea—using progressively masked images as a ranked list to ground responses in object visibility—is intuitive and could influence subsequent work on hallucination mitigation.

major comments (4)
  1. [Section 3.2, Eq. (1)] The load-bearing assumption that the likelihood of the chosen text w decreases monotonically with the mask fraction is not verified. For chosen responses that describe multiple objects, attributes, or scene context, masking 20–40% of one bounding box may leave the full response well-supported, so the sequence-level likelihood S_k can be flat or non-monotone. The paper should measure the ordering of S_k on a held-out sample before training and report the frequency with which monotonicity holds, and also report the absolute values of S_L to demonstrate that the fully masked image is actually suppressed. Without such a check, the training signal cannot be attributed to object visibility rather than to arbitrary ordering constraints.
  2. [Algorithm 1, Line 10 vs Section 3.1] The verification criterion is contradictory. Section 3.1 states that the masked image is kept as a hard negative when the filtering model hallucinates (i.e., still outputs the chosen text w), and that another object is selected only if the model does not hallucinate. Algorithm 1, Line 10 instead says to retry with another object when the filtering model 'answers (x_L,q,w) to be positive answer,' which is precisely the hallucinating case. This internal inconsistency must be resolved, as it changes which examples are included in training.
  3. [Section 4, Tables 1–4, 8–10] The main results are single runs without error bars or statistical significance tests. Only Figure 5 reports variance, and only for one model and one benchmark. Given that some improvements over mDPO are small (e.g., Idefics2-8B on AMBER HalRate 0.36 vs 0.40 in Table 1), the claim of consistent improvement needs repeated runs or significance testing across the reported metrics.
  4. [Section 4.1, Baselines] HALVA, HA-DPO, and V-DPO are taken from their original papers rather than re-run under the same training dataset and budget, while DPO and mDPO are reproduced under the same setting. The paper acknowledges this 'for reference,' but the abstract's claim of outperforming 'state-of-the-art VLM preference optimization approaches' rests in part on these incomparable numbers. A fair comparison would retrain these baselines under the same protocol, or the scope of the claim should be narrowed to the reproduced baselines.
minor comments (5)
  1. [Eq. (1) and Algorithm 1] The listwise loss uses z as the list length in Eq. (1), while Algorithm 1 and the text use L; unify the notation.
  2. [Section 3.2] Typo: 'postive' should be 'positive.'
  3. [Throughout] The paper consistently renders the model name as 'LLaV A-v1.5' (with a space), which should be 'LLaVA-v1.5'; also the Figure 3 caption refers to 'Idefics-2B' instead of 'Idefics2-8B,' and Table 3 uses 'V .P.' with spaces.
  4. [Appendix A, Table 6] Hyperparameters beta and delta (used in the losses) are not listed in Table 6; the temperature and anchor margin should be specified for reproducibility.
  5. [Figure 5] The legend uses 'MDPO' in capitals, inconsistent with the 'mDPO' notation used elsewhere.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: LPOI is an empirical training method evaluated on external hallucination benchmarks, with no fitted parameter renamed as a prediction.

full rationale

The paper's central claim is that listwise preference optimization over progressively masked images reduces VLM hallucination. This claim is supported by training three base VLMs with a fixed objective (Eq. 1) and measuring performance on MMHalBench, AMBER, and Object HalBench, which are external to the training data and are not constructed from the method's own outputs. The listwise loss enforces S_1 > S_2 > ... > S_L by construction, but that is a training objective, not a prediction derived from fitted constants; the paper never claims to predict a benchmark number from the same data used to fit a parameter. Reusing the chosen text w as the target for masked images is a modeling assumption, and the unverified monotonicity of S_k with mask fraction is a genuine correctness risk, but neither reduces the benchmark results to the training objective by definition. The hard-negative filtering step uses Idefics2-8B's hallucination behavior to select training examples, which is a data-selection heuristic rather than a fitted parameter later called a prediction. There is no load-bearing self-citation chain, no imported uniqueness theorem, and no renamed empirical pattern presented as derivation. The apparent discrepancy between Algorithm 1's filtering condition and Section 3.1's description is a wording issue, not a circular step. The paper's derivation is therefore self-contained relative to its external evaluations.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

LPOI introduces no new physical or conceptual entities; its machinery is composed of existing components (object detection, masking, visual prompting, listwise loss). The ledger entries above capture the hand-chosen hyperparameters and the domain assumptions that are load-bearing for the method's success.

free parameters (4)
  • beta (DPO/listwise temperature)
    Scales the log-probability ratios in LDPO, LAnchor, and LListwise. Standard DPO hyperparameter, value not stated in main text; affects the strength of the preference signal.
  • delta (anchor margin)
    Margin in LAnchor, borrowed from mDPO; chosen by hand to push the chosen text probability above a threshold on the original image.
  • List size L = 5 (default; 3,4,5 tested)
    Number of images in the list. Table 4 shows performance varies with L; larger L generally improves results, so the method's efficacy depends on this choice.
  • Masking ratio schedule = (k-1)/(L-1) x 100%, from edge
    Linear interpolation of the masked fraction of the bounding box. This specific schedule is an ad hoc modeling choice; no principled derivation is given.
assumptions (4)
  • domain assumption Grounding-DINO-Tiny localizes the text-relevant object accurately enough for the masks to remove the object from the model's visual input.
    Section 3.1 uses Grounding-DINO to select objects; if detection fails, the masked images do not actually hide the critical object and the ranking signal is corrupted.
  • ad hoc to paper The chosen text w remains the correct response for all partially masked images, with likelihood monotonically decreasing in masking ratio.
    Section 3.2 and Eq. (1) use the same w for every list position with a ranking target; no ground truth for intermediate masks is available, and an explicit 'object absent' text is never used.
  • domain assumption The base model's hallucination on a fully masked image is a valid indicator that the masked image is a useful hard negative.
    Algorithm 1 Lines 10-12 retain only samples where the verification model (Idefics2-8B) hallucinates the object; this assumes the failure transfers to other target models.
  • standard math Listwise ranking loss from Cao et al. (2007) and LiPO is a valid objective for optimizing the desired order.
    The loss in Eq. (1) is taken from the learning-to-rank literature without new proof; accepted as standard.

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Cite this review

Pith. "Pith review of LPOI: Listwise Preference Optimization for Vision Language Models." pith.science (2026). https://pith.science/paper/LIAPXWD2

@misc{pith2026250521061,
  author       = {Pith},
  title        = {Pith review of: LPOI: Listwise Preference Optimization for Vision Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LIAPXWD2}},
  note         = {Machine review of arXiv:2505.21061}
}
read the original abstract

Aligning large VLMs with human preferences is a challenging task, as methods like RLHF and DPO often overfit to textual information or exacerbate hallucinations. Although augmenting negative image samples partially addresses these pitfalls, no prior work has employed listwise preference optimization for VLMs, due to the complexity and cost of constructing listwise image samples. In this work, we propose LPOI, the first object-aware listwise preference optimization developed for reducing hallucinations in VLMs. LPOI identifies and masks a critical object in the image, and then interpolates the masked region between the positive and negative images to form a sequence of incrementally more complete images. The model is trained to rank these images in ascending order of object visibility, effectively reducing hallucinations while retaining visual fidelity. LPOI requires no extra annotations beyond standard pairwise preference data, as it automatically constructs the ranked lists through object masking and interpolation. Comprehensive experiments on MMHalBench, AMBER, and Object HalBench confirm that LPOI outperforms existing preference optimization methods in reducing hallucinations and enhancing VLM performance. We make the code available at https://github.com/fatemehpesaran310/lpoi.

Figures

Figures reproduced from arXiv: 2505.21061 by the authors.

Figure 1
Figure 1. Comparison of preference optimization (PO) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the LPOI framework. (1) Given an input image, prompt and corresponding set of chosen and [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Human evaluation results on a subset of the [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: MMHalBench results for different preference [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Qualitative results of the base model (LLaVA-v1.5-7B) its finetuned versions with DPO, mDPO, and [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Comparison of saliency maps with or without visual prompting (highlighted in red circle). [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Qualitative results of the base model (LLaVA-v1.5-7B) its variants optimized with DPO, mDPO, and [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: User interface and instruction for human evaluation. [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]

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    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.