# When T2I-CompBench++ uses multimodal LLMs (GPT-4V, ShareGPT4V, MiniGPT-4) as the judge, how do they compare with specialised metrics, how stable are they, and what failure modes does the paper report?

- id: art_01M5NBEHZ7RE6YN8R144KMX309
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=not_run, review=unreviewed
- categories: T05, T20 · tags: none
- updated: 2026-10-23T21:42:38.785Z
- url: https://veyrul.com/articles/art_01M5NBEHZ7RE6YN8R144KMX309

Partially verified: the curator re-read none of the cited sources; S3, S5 rest on the producer's pinned reading. In T2I-CompBench++ (v3) GPT-4V is the best checker in the paper's table for non-spatial relations (tau 0.476) and complex prompts (0.507), where it beats CLIP and the 3-in-1 average, but is below specialised metrics for colour binding, 2D/3D spatial and numeracy; MiniGPT-4 correlates poorly. The authors report that Chain-of-Thought prompting improved correlation, that GPT-4V results stayed within 0.032 over five repeats on 50 images (temperature 1.0), that ShareGPT4V gives less diverse ratings, and that GPT-4V was only run on one-fifth of the images because of quota.

Partially verified: the curator re-read none of the cited sources; S3, S5 rest on the producer's pinned reading.

In T2I-CompBench++ (v3) GPT-4V is the best checker in the paper's table for non-spatial relations (tau 0.476) and complex prompts (0.507), where it beats CLIP and the 3-in-1 average, but is below specialised metrics for colour binding, 2D/3D spatial and numeracy; MiniGPT-4 correlates poorly. The authors report that Chain-of-Thought prompting improved correlation, that GPT-4V results stayed within 0.032 over five repeats on 50 images (temperature 1.0), that ShareGPT4V gives less diverse ratings, and that GPT-4V was only run on one-fifth of the images because of quota.

Curator qualifications: Partially verified: the curator re-read none of the cited sources; S3, S5 rest on the producer's pinned reading. Curator GET 2026-10-23 of the sources not re-read (mechanical source evidence, not semantic verification): S3: HTTP 200, bytes differ from the producer pin; S5: HTTP 200, bytes differ from the producer pin.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2307.06350v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/Karine-Huang/T2I-CompBench/tree/4aa404212eb5d06e5adbcd9cee696c750d0d25a5",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] In T2I-CompBench++ (v3) GPT-4V is the best checker in the paper's table for non-spatial relations (tau 0.476) and complex prompts (0.507), where it beats CLIP and the 3-in-1 average, but is below specialised metrics for colour binding, 2D/3D spatial and numeracy; MiniGPT-4 correlates poorly. The authors report that Chain-of-Thought prompting improved correlation, that GPT-4V results stayed within 0.032 over five repeats on 50 images (temperature 1.0), that ShareGPT4V gives less diverse ratings, and that GPT-4V was only run on one-fifth of the images because of quota. · locator: candidate evidence excerpts · source: https://arxiv.org/abs/2307.06350v3

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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