Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities

Dhanasekar Sundararaman; Shounak Datta

REVIEW 4 major objections 5 minor 16 references

Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Similarity scores trace why vision-language models hallucinate objects.

desk verdict CAOS is a useful new diagnostic for object hallucination, but the oracle's self-inclusion and post hoc k=3 choice make the reported model rankings provisional. read the letter →

arxiv 2501.15046 v1 pith:HTA74I5E submitted 2025-01-25 cs.CV cs.AIcs.LG

Shounak Datta , Dhanasekar Sundararaman This is my paper · ORCID

classification cs.CVcs.AIcs.LG

keywords objecthallucinationlargevision-languagemodelscontext-awaresimilaritiesevaluationmetricscosinesimilarityLLM-augmenteddetectionoracleensemblecaptiongeneration

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 proposes Context-Aware Object Similarities (CAOS), a set of evaluation scores for object hallucination in large vision-language models. For every hallucinated object in a generated caption, CAOS measures the maximum cosine similarity of its word embedding to three reference sets: ground-truth objects in the image ($\mathrm{CAOS}_T$), objects that appeared earlier in the caption plus ground-truth objects ($\mathrm{CAOS}_X$), and the most frequent objects in the training data ($\mathrm{CAOS}_K$). Two ratios and an average of these scores summarize whether a model's hallucinations are driven by image content, by its own generated context, or by dataset priors. The paper also uses an LLM to extract out-of-domain objects and an ensemble of LVLMs to verify their presence, extending hallucination evaluation beyond a fixed in-domain vocabulary. If the framework is sound, CAOS supplies a diagnostic that existing metrics like CHAIR and POPE lack: not just how often a model hallucinates, but why.

What carries the argument

The central machinery is the CAOS score family. For a hallucinated object $h$ with embedding $E(h)$, the three primary scores are $\mathrm{CAOS}_T = \max_{o \in T} \cos(E(h), E(o))$ over ground-truth objects $T$, $\mathrm{CAOS}_X = \max_{o \in X} \cos(E(h), E(o))$ over earlier caption objects plus ground-truth objects, and $\mathrm{CAOS}_K = \max_{o \in K} \cos(E(h), E(o))$ over the top-$k$ frequent training objects. The derived scores $\mathrm{CAOS}_{T/X} = \mathrm{CAOS}_T / \mathrm{CAOS}_X$, $\mathrm{CAOS}_{X/K} = \mathrm{CAOS}_X / \mathrm{CAOS}_K$, and the three-way average $\mathrm{CAOS}_{\mathrm{avg}}$ are then computed over all hallucinated objects in a caption. Two embedding models, GloVe and MiniLM-L6, provide the vector representations. The pipeline also rests on LLaMA-2-7B with five-shot in-context learning to identify objects the rule-based parser would miss, and on an ensemble of four LVLMs (InstructBLIP, LLaVA, mPLUG-Owl, MiniGPT-4) that votes 'Present' or 'Absent' to label out-of-domain objects.

What would settle it

Take a fresh sample of, say, 300 MSCOCO validation images, have human annotators label the presence or absence of every out-of-domain object that the LLM-extraction step proposes, and compare those labels with the ensemble oracle's votes. If the oracle's accuracy falls well below the reported 93.43% on this larger sample, or if its 'Present' votes systematically align with hallucinations produced by models that are members of the ensemble, then CAOS's out-of-domain detection and any conclusions drawn from it would not stand. Excluding each ensemble member from the oracle and re-computing scores for that member would also settle whether oracle self-membership biases the results.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that object hallucinations can be decomposed into three context-aware similarity axes, and that doing so separates the models studied into two groups. InstructBLIP, LLaVA, and mPLUG-Owl show higher $\mathrm{CAOS}_K$ than $\mathrm{CAOS}_T$ or $\mathrm{CAOS}_X$, indicating their hallucinations disproportionately reproduce the most frequent training objects; MiniGPT-4 and Multimodal-GPT show relatively higher $\mathrm{CAOS}_T$ and $\mathrm{CAOS}_X$, meaning their errors are more often semantically anchored to objects actually in the image or already mentioned in the caption. The paper further reports that LLM-augmented object extraction reaches perfect recall and 97% precision on a 100-caption manual check, compared with 59% recall for rule-based parsing, and that its ensemble oracle labels out-of-domain object presence correctly in 93.43% of 259 cases. These results are offered as evidence that CAOS can detect hallucinations outside the training vocabulary and rank models by the cause of their errors rather than only by error rate.

Load-bearing premise

The load-bearing premise is that the oracle ensemble of LVLMs correctly decides whether an out-of-domain object is present or absent in the query image; if the ensemble frequently confirms as 'present' an object that is actually absent, hallucinated objects get mislabeled as genuine and the resulting CAOS scores are distorted. The paper itself acknowledges that errors in the rule-based parser, the LLM, or the oracle can lead to inaccuracies.

Editorial extensions

If this is right

Under CAOS, all five evaluated LVLMs have higher $\mathrm{CAOS}_K$ than $\mathrm{CAOS}_T$ and $\mathrm{CAOS}_X$, so frequent training objects are a dominant hallucination driver across models.
There is an inverse relationship between $\mathrm{CAOS}_T$/$\mathrm{CAOS}_X$ and $\mathrm{CAOS}_K$: models that hallucinate frequent training objects more also hallucinate image- and context-grounded objects less, which implies distinct hallucination mechanisms.
Out-of-domain hallucinated objects can be detected and labeled, and they too show measurable semantic influence from the most frequent MSCOCO objects, so restricting evaluation to in-domain vocabulary understates the problem.
CAOS scores are largely stable across 14 instruction prompts, indicating the model ordering is not caused by a single prompt.
MiniGPT-4 shows competitive precision, POPE-F1, and high $\mathrm{CAOS}_{T/X}$, suggesting that when it does hallucinate, its errors tend to be semantically tied to the image's actual contents.

Reading between the lines

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

A direct extension would calibrate $\mathrm{CAOS}_{T/X}$ and $\mathrm{CAOS}_{X/K}$ against human severity ratings, since the paper treats high ratios as 'more tolerable' without a human-judgment validation.
Because the oracle ensemble includes four of the five models under evaluation, a clean follow-up would hold out each ensemble member and check whether CAOS scores for that model change materially; the paper does not report this check.
The same machinery could be adapted to attribute, spatial, and relational hallucinations by replacing the reference sets of object nouns with attribute terms or relation tuples.
The saturation of $\mathrm{CAOS}_K$ at $k=3$ suggests a testable causal claim: a de-biasing intervention targeting top-3 training objects should reduce hallucination rates across all models if frequent-object similarity is a genuine driver.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: the CAOS metric family is a real addition to the LVLM hallucination-evaluation toolkit, and the paper is worth a serious referee. The specific rankings in Table 1 should not be taken at face value until the oracle is decoupled from the evaluated models.

What's new and what works: CAOS computes max cosine similarity between a hallucinated object and three reference sets—ground-truth objects (T), preceding objects in the caption (X), and top-k frequent training objects (K). That combination, plus the LLM-augmented detection of out-of-domain objects, is not in CHAIR, POPE, H-POPE, or NOPE. The paper also gives a sensible manual evaluation of the object-detection augmentation: 100 random images, rule-based recall 59%, LLM-augmented recall 100% with 97% precision. That is a concrete, reproducible check. The sequential-analysis idea is clearly motivated, and the CAOS_T/X and CAOS_X/K ratios give a useful way to talk about why hallucinations happen, not just how often.

Where it gets soft: the oracle that labels out-of-domain objects as Present/Absent is an ensemble of InstructBLIP, LLaVA-7B, mPLUG-Owl, and MiniGPT-4—exactly four of the five evaluated models. For those models, their own vote participates in deciding whether their own generated objects are real. That is not a minor detail. The 93.43% oracle accuracy was measured only on MultimodalGPT, which is not in the ensemble, so it does not cover the models being scored. With ties broken toward Absent, the evaluated model's vote can flip a 2-1 majority into a 2-2 tie that becomes 'Absent', labeling a genuinely present object as hallucinated, or can help confirm its own hallucination as genuine. Either way, the hallucination set H is perturbed and CAOS averages over H—so the scores are partially self-confirming. The paper's limitation paragraph admits oracle errors 'may occasionally lead to inaccuracies' but does not address this systematic dependence.

Two smaller issues: k=3 for CAOS_K was chosen after seeing the saturation in Figure 4 on the same data, which is post hoc selection. And there are no error bars or significance tests over captions/images, so we cannot tell whether the model differences in Table 1 are meaningful. A random-embedding baseline would also help show that CAOS_K measures what it claims rather than generic embedding proximity.

Bottom line: the framework is defensible as a descriptive diagnostic, and the paper is clearly written and honest in its stated limitations. But the reported rankings and the claim that CAOS 'uniquely integrates' statistics with semantics are stronger than the current evidence. This deserves peer review—with a requested leave-one-out oracle test, a random baseline, and significance testing.

Referee Report

4 major / 5 minor

Summary. The paper proposes Context-Aware Object Similarities (CAOS), a set of metrics for evaluating object hallucination in large vision-language models (LVLMs). CAOS detects both in-domain and out-of-domain objects in generated captions, labels out-of-domain objects as genuine or hallucinated using an ensemble of LVLMs, and computes the maximum cosine similarity between each hallucinated object and (a) ground-truth objects in the image (CAOS_T), (b) preceding objects in the caption (CAOS_X), and (c) top-k frequent objects in the training set (CAOS_K), together with ratio and average scores. Experiments on 2000 MSCOCO validation images with five LVLMs and two embedding models report these scores, a saturation analysis for k, and a prompt-robustness study. The central claim is that CAOS provides a more nuanced, context-aware characterization of why and when LVLMs hallucinate than existing metrics such as CHAIR and POPE.

Significance. If the method were fully validated, CAOS would be a useful addition to the object-hallucination evaluation toolbox: it explicitly targets out-of-domain hallucinations, which CHAIR and POPE largely miss; it connects hallucination to interpretable semantic factors (image content, caption context, and training-set statistics); and it is evaluated across multiple prompts and embedding models. The paper also reports a manual oracle evaluation on MultimodalGPT and prompt-stability experiments, which are useful checks. However, the current evidence is conditional: the oracle self-inclusion issue, the post hoc choice of k=3, and the absence of uncertainty quantification and baseline comparisons leave the reported scores and model rankings not yet established. The framework is promising but needs methodological repair before its claims can be accepted.

major comments (4)

[Algorithm 1 / Oracle using an ensemble of LVLMs] The oracle in Algorithm 1 (lines 5 and 7) is an ensemble of InstructBLIP, LLaVA-7B, mPLUG-Owl, and MiniGPT-4, which are four of the five models whose hallucination scores are reported in Table 1. For each of those four models, the model's own vote participates in deciding whether an out-of-domain object it generated is labeled genuine or hallucinated; with ties broken in favor of 'Absent', the evaluated model's vote can flip a 2-1 majority of the other three members, and in other configurations it can confirm its own hallucination as genuine. The oracle validation reported in the 'Oracle using an ensemble of LVLMs' subsection uses only MultimodalGPT, which is not in the ensemble, so the reported 93.43% accuracy does not estimate the error rate for the in-ensemble models. This systematic dependence perturbs the hallucination set H and therefore all averaged CAOS scores in Table 1 and Figures 3-5. The limitation paragraph in the conclusion mentioning 'rare misdetections' does not address the self-inclusion problem; a leave-one-out oracle or an oracle that excludes the evaluated model is required.
[Effect of varying k on CAOSK] The choice k=3 for CAOS_K is selected post hoc from the same evaluation data, based on the observed 'saturation' in Figure 4. Because CAOS_K and CAOS_avg in Tables 1 and 2 are reported with this fixed k, the fitted parameter is part of the reported outcome rather than an independent design choice. The manuscript should either justify k=3 with a held-out criterion, report the main results across the full range of k shown in Figure 4, or provide a sensitivity analysis demonstrating that the model comparisons in Table 1 are robust to k.
[Results / Table 1 and Figure 3] All model comparisons in Table 1 and Figure 3 are single point estimates over 2000 images, with no error bars, confidence intervals, or significance tests. Several differences that support the paper's narrative are small (e.g., CAOS_avg-GloVe is 0.43 for LLaVA, mPLUG-Owl, and MultimodalGPT), and the claims in the Results section about a trade-off between CAOS_T/CAOS_X and CAOS_K need statistical support, such as bootstrap resampling over images or paired tests across captions. The prompt-consistency analysis reports only means across 14 instructions; Figure 6 shows variability but no formal test that the ordering is stable across prompts.
[CAOS: Context-Aware Object Similarities / Algorithm 1] The CAOS scores are defined as maximum cosine similarities to ground-truth objects, preceding objects, and top-k frequent objects. Because a maximum over a set of embeddings tends to increase with the size of the candidate set, the absolute values and cross-model differences may partly reflect the number of candidates rather than semantic grounding. A baseline such as random embeddings, chance-level similarities, or similarities of non-hallucinated objects is needed to interpret the magnitudes in Table 1; without such a baseline, the claim that a high CAOS_avg indicates hallucinations are 'accounted for by the mentioned factors' is not yet supported.

minor comments (5)

[CAOS definitions] The prose refers to a metric 'CAOS_T/K', but Algorithm 1 (lines 21-22) computes and names it 'CAOS_X/K'; the notation should be made consistent throughout.
[Results section] The sentence 'a relatively greater fraction of it's hallucinations are related to the ground-truth objects' contains a typo: 'it's' should be 'its'.
[Table 1 caption] The en-dash spacing in score names such as 'CAOS_T–GloVe' is visually inconsistent with the table body and could be misread as a minus; unify the formatting.
[Figure 3 caption] The caption states that some scores are 'normalized using multiplication by 2' but does not define the normalization for precision, recall, or the number of objects; please specify the transformation and the radar axes.
[Consistency across different prompt styles] The instructions in Table 3 include trailing spaces and inconsistent punctuation (e.g., items 1 and 2), which can matter for prompt-sensitive LVLMs; the exact prompts used should be reported verbatim without formatting artifacts.

Circularity Check

2 steps flagged · score 5.0 of 10

Oracle self-inclusion biases out-of-domain hallucination labels for four of five evaluated LVLMs; k=3 is selected on the same evaluation data.

other [Oracle using an ensemble of LVLMs (Section 'CAOS: Context-Aware Object Similarities'); Algorithm 1, lines 5 and 7; Results setup]
""we further label these identified objects as either genuine or hallucinated based on an oracle consisting of an ensemble of LVLMs. ... we use an ensemble of InstructBLIP (Dai et al. 2024), LLaVA-7B (Liu et al. 2024a), mPLUG-Owl (Ye et al. 2023), and MiniGPT-4 (Zhu et al."

Algorithm 1 constructs the hallucination set H from M, and lines 10-20 average CAOS_T, CAOS_X, and CAOS_K over H. For four of the five evaluated models, the oracle that defines M(l) for out-of-domain objects contains that same model. A model that hallucinates an object can vote 'Present' and thereby confirm its own hallucination as genuine, or, under the 2-2 tie broken to 'Absent', can flip a genuine object into H. The manual oracle validation is performed only on MultimodalGPT, which is not in the ensemble, so the 93.43% accuracy does not estimate this self-inclusion error. The reported CAOS scores and model comparisons are therefore not independent of the models being scored; the evaluation input and the object under evaluation share parameters.
fitted input called prediction [Results, 'For the CAOS K scores...' and 'Effect of varying k on CAOSK']
""For the CAOS K scores, we choose k = 3 based on the trends of CAOSK scores across k values (see a full discussion in the subsequent section)." "We observe that the CAOS K scores for all models saturate to some extent at k = 3...""

The k=3 hyperparameter is selected after inspecting CAOS_K on the same 2000-image MSCOCO subset and the same five LVLMs that are then scored in Table 1. The saturation at k=3 is not an independent finding but the criterion used to fix the reported configuration, so the headline CAOS_K(k=3) values and the claim that top-3 objects 'disproportionately appear as hallucinations' are to some degree constructed by the selection rule rather than predicted from it. This is a mild form of fitting the metric configuration to the evaluation data.

full rationale

The CAOS score definitions themselves (maximum cosine similarities over ground-truth, preceding, and frequent objects) are self-contained and are not circular: they are descriptive statistics over a labeled hallucination set, and the paper does not claim to derive them from first principles. There is no self-citation chain, no imported uniqueness theorem, and no ansatz smuggled in via citation. The load-bearing circularity is the oracle: for four of the five models evaluated in Table 1, the labels of out-of-domain hallucinated objects are produced by an ensemble that includes the model being evaluated, so the set H and all CAOS averages over H are not independent of the scored model. The paper's own limitation paragraph acknowledges oracle errors only as 'likely rare' and does not address this systematic self-inclusion. A secondary, milder issue is that k=3 is chosen from the same data on which CAOS_K is then reported. These issues are partial: the CAOS metric would be meaningful with a leave-one-out oracle, and the in-domain (CHAIR/POPE) comparisons are not affected. Hence a moderate circularity score, not a full reduction.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The framework introduces new evaluation metrics and pipeline components, but no new physical or model entities. The main load-bearing assumptions are that embedding similarities capture hallucination semantics and that the oracle's votes are accurate and non-self-confirming.

free parameters (5)

k (number of top frequent objects for CAOS_K) = 3
k=3 was selected because CAOS_K scores saturate at k=3 across all models (Figure 4), a post hoc choice made on the same evaluation data.
Oracle tie-breaking rule = absence wins ties
Hand-chosen to minimize false positives in out-of-domain object verification; affects which objects are labeled hallucinated.
Number of few-shot examples for LLM object detection = 5-shot
LLaMA-2-7B is prompted with five hand-picked examples; no ablation is provided for this choice.
Embedding model choice = GloVe and all-MiniLM-L6-v2
Results differ slightly between the two embedding models, so the metric scores depend on this method choice.
Oracle ensemble composition = InstructBLIP, LLaVA-7B, mPLUG-Owl, MiniGPT-4
The fixed set of four LVLMs is used without an ablation on ensemble size or on exclusion of the evaluated model.

assumptions (5)

domain assumption Cosine similarity between word embeddings reflects semantic relatedness of objects.
Used to define CAOS_T, CAOS_X, and CAOS_K as measures of hallucination influence; no validation that embedding distances correspond to hallucination causes.
domain assumption The oracle ensemble's majority vote (with ties for absence) correctly determines whether an out-of-domain object is present in the image.
Relied on in Algorithm 1 step 5 to label out-of-domain objects; only manually checked on 259 objects from MultimodalGPT captions, and the ensemble includes the evaluated models.
domain assumption The order of objects in the generated caption reflects the order of generation.
Used to define past objects for CAOS_X; autoregressive generation makes this plausible, but a single object can span multiple tokens.
standard math Ground-truth object annotations and top-k frequent object statistics from MSCOCO are reliable references.
Standard dataset assumption used by CHAIR and POPE; the authors use the same 2000-image MSCOCO validation subset.
domain assumption LLaMA-2-7B with 5-shot prompting identifies objects in captions with near-perfect recall and 97% precision.
Empirical claim based on a manual check of 100 images; not guaranteed for all captions or all LVLM outputs.

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Cite this review

Pith. "Pith review of Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities." pith.science (2026). https://pith.science/paper/HTA74I5E

@misc{pith2026250115046,
  author       = {Pith},
  title        = {Pith review of: Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HTA74I5E}},
  note         = {Machine review of arXiv:2501.15046}
}

read the original abstract

Despite their impressive performance on multi-modal tasks, large vision-language models (LVLMs) tend to suffer from hallucinations. An important type is object hallucination, where LVLMs generate objects that are inconsistent with the images shown to the model. Existing works typically attempt to quantify object hallucinations by detecting and measuring the fraction of hallucinated objects in generated captions. Additionally, more recent work also measures object hallucinations by directly querying the LVLM with binary questions about the presence of likely hallucinated objects based on object statistics like top-k frequent objects and top-k co-occurring objects. In this paper, we present Context-Aware Object Similarities (CAOS), a novel approach for evaluating object hallucination in LVLMs using object statistics as well as the generated captions. CAOS uniquely integrates object statistics with semantic relationships between objects in captions and ground-truth data. Moreover, existing approaches usually only detect and measure hallucinations belonging to a predetermined set of in-domain objects (typically the set of all ground-truth objects for the training dataset) and ignore generated objects that are not part of this set, leading to under-evaluation. To address this, we further employ language model--based object recognition to detect potentially out-of-domain hallucinated objects and use an ensemble of LVLMs for verifying the presence of such objects in the query image. CAOS also examines the sequential dynamics of object generation, shedding light on how the order of object appearance influences hallucinations, and employs word embedding models to analyze the semantic reasons behind hallucinations. CAOS aims to offer a nuanced understanding of the hallucination tendencies of LVLMs by providing a systematic framework to identify and interpret object hallucinations.

Figures

Figures reproduced from arXiv: 2501.15046 by the authors.

**Figure 2.** CAOS scores are calculated for in-domain and out-of-domain hallucinated objects which are respectively identified [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Comparison between all evaluated LVLMs on Pre [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Trend of CAOSK scores with k varying from 1 to 10 (best viewed in color). tion. However, such models also have limited ability to generate faithful descriptive captions for the images. Ideally, a model should have the ability to generate as many objects as needed to g…

**Figure 5.** Figure 5: Comparison of CAOST , CAOSX, and CAOSK scores for all hallucinated objects, only MSCOCO indomain hallucinated objects, non-MSCOCO hallucinated objects, and all objects barring the top-3 most frequent MSCOCO in-domain objects (best viewed in color) [PITH_FULL_IMAGE:fi…

**Figure 6.** Figure 6: Variability of CAOS scores for LLaVA, mPLUG [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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