title: Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language
    Models through Visual Counterfacts
summary: Multimodal Large Language Models (MLLMs) perform well on tasks such as visual
  question answering, but it remains unclear whether their reasoning relies more
  on memorized world knowledge or on the visual information present in the input
  image. To investigate this, we introduce Visual CounterFact, a new dataset of
  visually-realistic counterfactuals that put world knowledge priors (e.g, red
  strawberry) into direct conflict with visual input (e.g, blue strawberry).
  Using Visual CounterFact, we show that model predictions initially reflect
  memorized priors, but shift toward visual evidence in mid-to-late layers. This
  dynamic reveals a competition between the two modalities, with visual input
  ultimately overriding priors during evaluation. To control this behavior, we
  propose Pixels Versus Priors (PvP) steering vectors, a mechanism for
  controlling model outputs toward either world knowledge or visual input through
  activation-level interventions. On average, PvP successfully shifts 92.5% of
  color and 74.6% of size predictions from priors to counterfactuals. Together,
  these findings offer new tools for interpreting and controlling factual
  behavior in multimodal models.
paper_authors._id: 683f4b081a2d176679fab869
paper_authors.name: Michal Golovanevsky
paper_authors._id: 683f4b081a2d176679fab86a
paper_authors.name: William Rudman
paper_authors._id: 683f4b081a2d176679fab86b
paper_authors.name: Michael Lepori
paper_authors._id: 683f4b081a2d176679fab86c
paper_authors.name: Amir Bar
paper_authors._id: 683f4b081a2d176679fab86d
paper_authors.name: Ritambhara Singh
paper_authors._id: 683f4b081a2d176679fab86e
paper_authors.name: Carsten Eickhoff
paper_title: Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language
    Models through Visual Counterfacts
paper_summary: Multimodal Large Language Models (MLLMs) perform well on tasks such as visual
  question answering, but it remains unclear whether their reasoning relies more
  on memorized world knowledge or on the visual information present in the input
  image. To investigate this, we introduce Visual CounterFact, a new dataset of
  visually-realistic counterfactuals that put world knowledge priors (e.g, red
  strawberry) into direct conflict with visual input (e.g, blue strawberry).
  Using Visual CounterFact, we show that model predictions initially reflect
  memorized priors, but shift toward visual evidence in mid-to-late layers. This
  dynamic reveals a competition between the two modalities, with visual input
  ultimately overriding priors during evaluation. To control this behavior, we
  propose Pixels Versus Priors (PvP) steering vectors, a mechanism for
  controlling model outputs toward either world knowledge or visual input through
  activation-level interventions. On average, PvP successfully shifts 92.5% of
  color and 74.6% of size predictions from priors to counterfactuals. Together,
  these findings offer new tools for interpreting and controlling factual
  behavior in multimodal models.
paper_ai_summary: Visual CounterFact and PvP steering vectors help interpret and control the competition between visual input and memorized world knowledge in multimodal large language models.
paper_ai_keywords: Multimodal Large Language Models Visual CounterFact visually-realistic counterfactuals world knowledge priors visual input model predictions mid-to-late layers Pixels Versus Priors (PvP) steering vectors activation-level interventions