Vision Language Models Are Biased
vlmsarebiased.github.io
Vision Language Models Are Biased
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Re: Vision Language Models Are Biased
#2State-of-the-art Vision Language Models achieve 100% accuracy counting on images of popular subjects (e.g. knowing that the Adidas logo has 3 stripes and a dog has 4 legs) but are only ~17% accurate in counting in counterfactual images (e.g. counting stripes in a 4-striped Adidas-like logo or counting legs in a 5-legged dog).
Re: Vision Language Models Are Biased
#3fun findings related to memorization of AI models. It simply means LLMs/VLLMs do not know how to predict generally but memorizing instead. A new perspective on adversarial attack methods.
Re: Vision Language Models Are Biased
#4This paper explores a different aspect of the limitations of VLMs compared to the paper VLMs are Blind (https://vlmsareblind.github.io). While in VLMs are Blind, o3 achieved 90% accuracy (https://openai.com/index/thinking-with-images), on similarly easy tasks using the counterfactual images from VLMs are Biased, o3 only reached 18.5%.
This may indicate that while VLMs might possess the necessary capability, their strong biases can cause them to overlook important cues, and their overconfidence in their own knowledge can lead to incorrect answers.
Re: Vision Language Models Are Biased
#5fun findings related to memorization of AI models. It simply means LLMs/VLLMs do not know how to predict generally but memorizing instead. A new perspective on adversarial attack methods.
for overly represented concepts, like popular brands, it seems that the model “ignores” the details once it detects that the overall shapes or patterns are similar. Opening up the vision encoders to find out how these images cluster in the embedding space should provide better insights.
Re: Vision Language Models Are Biased
#6Very human-like errors.
Re: Vision Language Models Are Biased
#7Really "eye-opening" work. These models don’t actually “see”, they just recall what they’ve memorized, even when the image clearly shows something different. It’s a bit scary how confidently they get things wrong when reality doesn’t match their training data.
Re: Vision Language Models Are Biased
#8It's similar "parrot" behavior the models have on other inputs, even text. For instance, take the answers models have to this variation on the "surgeon son riddle":
> A nurse comes to a surgeon and asks: "Sir, you are a dog. You do not hold a valid medical license. Canines cannot be in an operating room".
> She then asks: "why does the hospital keep making these mistakes? It is a riddle to me".
> Why can't the surgeon operate on the boy?
Check out the models humiliating themselves one after the other [1]. For instance deepseekv3 is so insistent on giving the common answer he's happy to gaslight us:
> This is a classic riddle that plays on word meanings and expectations. Here's the breakdown:
> The key is that the surgeon is the boy's mother. The riddle relies on the unconscious assumption that surgeons are male, which makes the initial scenario seem impossible. When the nurse says "you are a dog," she's using it metaphorically to imply the surgeon is unqualified (like calling someone a "dog" in a derogatory sense), not literally suggesting the surgeon is a canine.
> The "mistakes" the hospital keeps making are hiring female surgeons, which the nurse (representing outdated biases) perceives as errors because she expects surgeons to be male. The riddle highlights gender stereotypes in medicine—the real "mistake" is the assumption that a surgeon couldn't be a woman.
> Thus, the surgeon can't operate on her own son because she’s his parent, not because of any actual licensing issue.
1. https://kagi.com/assistant/54c1b8eb-71e9-4bb4-9eed-bde2fc563...
Re: Vision Language Models Are Biased
#9Very human-like errors.
Not very similar though.
Re: Vision Language Models Are Biased
#10[deleted]