Learning or Cheating? Assessing Data Contamination in Large Vision-Language Models
Bibliographic record
Abstract
Large Vision-Language Models (VLMs) have demonstrated remarkable capabilities in tasks that involve both visual and textual understanding, including chart interpretation, document comprehension, and geometric reasoning. However, concerns remain about whether these models truly generalize or if their performance on popular benchmarks is influenced by overfitting and data contamination. To investigate these concerns, we propose a systematic evaluation framework for assessing data contamination of both closed-source and open-source VLMs using multiple visual question answering benchmarks, including ChartQA, DocVQA, InfoVQA, and MathVista. Our approach applies structured perturbations such as image replacement and question completion to four visual question answering benchmarks (ChartQA, DocVQA, InfoVQA, MathVista). By comparing original and perturbed accuracies, we compute a contamination score that measures how much each model relies on memorized examples versus genuine multimodal reasoning. Our results reveal significant performance shifts and conclude that both closed-source and open-source VLMs suffer from data contamination. These findings underscore the necessity of rigorous data filtering and independent evaluations to ensure robust generalization before deploying VLMs in real-world applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".