Vision Language Models Are Biased

Memorization vs Visual Reasoning

• Many commenters interpret the results as evidence that VLMs heavily rely on memorized associations (“dogs have 4 legs”, “Adidas has 3 stripes”) rather than actually counting or visually parsing scenes.
• Errors are mostly “bias‑aligned”: models answer with the typical fact even when the image is a clear counterexample.
• This is linked to broader “parrot” behavior seen in text tasks (classic riddles with slightly altered wording still trigger stock answers).

Comparison to Human Perception

• Some argue the behavior is “very human‑like”: humans also use priors, often don’t scrutinize familiar stimuli, and can miss anomalies.
• Others strongly disagree: humans asked explicitly to count legs in a clear image would nearly always notice an extra/missing limb; VLM failures feel qualitatively different.
• Discussion touches on cognitive science (priors, inattentional blindness, Stroop effect, blind spots, hallucinations) but consensus is humans are much more sensitive to anomalies when prompted.

Experimental Replication and Variability

• Several people try examples with ChatGPT‑4o and report mixed results: some images are now handled correctly, others still fail.
• Speculation about differences between chat vs API models, prompts, system messages, and model updates; overall behavior appears inconsistent and somewhat opaque.
• Prior work (“VLMs are Blind”) is contrasted: models can perform well on simple perception tasks yet still crumble on slightly counterfactual variants.

Reliability and Downstream Impact

• Practitioners using VLMs for OCR and object pipelines report similar “looks right but wrong” behavior—especially dangerous because outputs match human expectations.
• Concern that such biased, overconfident errors would be far more serious in safety‑critical domains (self‑driving, medical imaging).
• Asking models to “double‑check” rarely fixes errors and often just re‑runs the same flawed reasoning.

Causes and Potential Fixes

• Viewed as a classic train/deploy distribution gap: training data almost never contains five‑legged dogs, four‑stripe Adidas, etc., so memorized priors dominate.
• Suggestions:
  • Explicitly train on counterfactuals and adversarial images.
  • Borrow ideas from fairness/unbalanced‑dataset research.
  • Emphasize counting/verification tasks during training or fine‑tuning.
  • Adjust attention mechanisms so the visual signal can override language priors.

Debate over “Bias”

• Some frame “bias” as inevitable: models are learned biases/statistics, not programs that follow explicit logic.
• Others distinguish:
  • Social bias (stereotypes),
  • Cognitive/semantic bias (facts like leg counts, logo structure),
  • And the normative sense of “unfair” bias.
• One thread notes that if the world and data are biased, models inheriting those patterns isn’t surprising—but commenters still expect them not to fail basic, concrete questions about what’s in front of them.