8️⃣ Work led by the super duo
✨ An Võ + Khải Nguyên Nguyễn ✨
w/ countless assists from Mohammad Taesiri, Tường Vy Đặng & Prof. Daeyoung Kim.

Code & data: vlmsarebiased.github.io
Paper: arxiv.org/abs/2505.23941

inspired by vlmsareblind.github.io

Thank you for any feedback 🙏

8/8

7️⃣ On a task where we create from scratch.

Q: Count the circles in cell C3.
🤖: 3 ❌

VLMs are only ~22% accurate and biased towards the surrounding cells.

7/8

6️⃣ Optical illusion is an interesting task. VLMs know all 6 illusions and their expected answers.

But, here we modify Ebbinghaus pattern so that two inner circles clearly differ in size. And...

o3: equal ❌
Sonnet 3.7: equal ❌

6/8

5️⃣ Bias exists across SIX domains of decreasing popularity (animals -> logos -> flags -> chess pieces -> optical illusion -> boardgames) and ONE domain where we create novel patterns that do not exist on the Internet. ⚠️

🟧 % of predictable, biased answers by VLMs.

5/8

4️⃣ We study bias using neutral counting questions (Q1/Q2) as opposed to setting up models to fail by a textual (adversarial?) prompt (Q3) as in prior work.

4/8

3️⃣ Via tests, VLMs 100% recognize ✅ every subject and its well-known visual elements (e.g. legs/stripes). But they fail ❌ to count on the counterfactual images

e.g.,: when

- extra leg added to 4-legged animals
- extra stripe added to 3-striped Adidas logo

3/8

2️⃣ Asking VLMs to examine carefully, use code/tools won't help since they are so (over)confident.

Hard to believe? 😅
Image to try yourself: 👇http://s.anhnguyen.me/250602__zebra_original_image.png

More examples: github.com/anvo25/vlms-...

2/8