title: Vision Language Models are Biased
summary: Large language models (LLMs) memorize a vast amount of prior knowledge from
  the Internet that help them on downstream tasks but also may notoriously sway
  their outputs towards wrong or biased answers. In this work, we test how the
  knowledge about popular subjects hurt the accuracy of vision language models
  (VLMs) on standard, objective visual tasks of counting and identification. We
  find that state-of-the-art VLMs are strongly biased (e.g, unable to recognize a
  fourth stripe has been added to a 3-stripe Adidas logo) scoring an average of
  17.05% accuracy in counting (e.g., counting stripes in an Adidas-like logo)
  across 7 diverse domains from animals, logos, chess, board games, optical
  illusions, to patterned grids. Insert text (e.g., "Adidas") describing the
  subject name into the counterfactual image further decreases VLM accuracy. The
  biases in VLMs are so strong that instructing them to double-check their
  results or rely exclusively on image details to answer improves counting
  accuracy by only +2 points, on average. Our work presents an interesting
  failure mode in VLMs and an automated framework for testing VLM biases. Code
  and data are available at: vlmsarebiased.github.io.
paper_authors._id: 683cf4405810d395f0a3788b
paper_authors.name: An Vo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T13:39:54.864Z
paper_authors._id: 6631fd5961a4305e5610d403
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6631fd5961a4305e5610d403/P1Dtxzn-KIbYDDsiw60nr.jpeg
paper_authors.fullname: An Vo
paper_authors.type: user
paper_authors.user: anvo25
paper_authors._id: 683cf4405810d395f0a3788c
paper_authors.name: Khai-Nguyen Nguyen
paper_authors._id: 683cf4405810d395f0a3788d
paper_authors.name: Mohammad Reza Taesiri
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-06-02T00:52:37.933Z
paper_authors._id: 6039478ab3ecf716b1a5fd4d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6039478ab3ecf716b1a5fd4d/_Thy4E7taiSYBLKxEKJbT.jpeg
paper_authors.fullname: taesiri
paper_authors.type: user
paper_authors.user: taesiri
paper_authors._id: 683cf4405810d395f0a3788e
paper_authors.name: Vy Tuong Dang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T13:39:52.070Z
paper_authors._id: 67a87266e113700e5ee1c3fc
paper_authors.avatarUrl: /avatars/ea21f5d239244b810c892333663b5390.svg
paper_authors.fullname: Tường Vy
paper_authors.type: user
paper_authors.user: tuongvy2603
paper_authors._id: 683cf4405810d395f0a3788f
paper_authors.name: Anh Totti Nguyen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-02T00:45:56.803Z
paper_authors._id: 653194a4c8da3465f4701ad1
paper_authors.avatarUrl: /avatars/6682164fcaf1d339ce9ac82ba131af5e.svg
paper_authors.fullname: Khai-Nguyen Nguyen
paper_authors.type: user
paper_authors.user: knguyennguyen
paper_authors._id: 683cf4405810d395f0a37890
paper_authors.name: Daeyoung Kim
paper_title: Vision Language Models are Biased
paper_summary: Large language models (LLMs) memorize a vast amount of prior knowledge from
  the Internet that help them on downstream tasks but also may notoriously sway
  their outputs towards wrong or biased answers. In this work, we test how the
  knowledge about popular subjects hurt the accuracy of vision language models
  (VLMs) on standard, objective visual tasks of counting and identification. We
  find that state-of-the-art VLMs are strongly biased (e.g, unable to recognize a
  fourth stripe has been added to a 3-stripe Adidas logo) scoring an average of
  17.05% accuracy in counting (e.g., counting stripes in an Adidas-like logo)
  across 7 diverse domains from animals, logos, chess, board games, optical
  illusions, to patterned grids. Insert text (e.g., "Adidas") describing the
  subject name into the counterfactual image further decreases VLM accuracy. The
  biases in VLMs are so strong that instructing them to double-check their
  results or rely exclusively on image details to answer improves counting
  accuracy by only +2 points, on average. Our work presents an interesting
  failure mode in VLMs and an automated framework for testing VLM biases. Code
  and data are available at: vlmsarebiased.github.io.
paper_ai_summary: Vision language models exhibit strong biases in counting and identification tasks, demonstrating a failure mode that persist even with additional instructions or context.
paper_ai_keywords: large language models vision language models downstream tasks popular subjects accuracy visual tasks counting identification biases counterfactual image automated framework