NL-Eye: Abductive NLI for Images
Authors: name: Mor Ventura, name: Michael Toker, name: Nitay Calderon, name: Zorik Gekhman, name: Yonatan Bitton, name: Roi Reichart

Will a Visual Language Model (VLM)-based bot warn us about slipping if it
detects a wet floor? Recent VLMs have demonstrated impressive capabilities, yet
their ability to infer outcomes and causes remains underexplored. To address
this, we introduce NL-Eye, a benchmark designed to assess VLMs' visual
abductive reasoning skills. NL-Eye adapts the abductive Natural Language
Inference (NLI) task to the visual domain, requiring models to evaluate the
plausibility of hypothesis images based on a premise image and explain their
decisions. NL-Eye consists of 350 carefully curated triplet examples (1,050
images) spanning diverse reasoning categories: physical, functional, logical,
emotional, cultural, and social. The data curation process involved two steps -
writing textual descriptions and generating images using text-to-image models,
both requiring substantial human involvement to ensure high-quality and
challenging scenes. Our experiments show that VLMs struggle significantly on
NL-Eye, often performing at random baseline levels, while humans excel in both
plausibility prediction and explanation quality. This demonstrates a deficiency
in the abductive reasoning capabilities of modern VLMs. NL-Eye represents a
crucial step toward developing VLMs capable of robust multimodal reasoning for
real-world applications, including accident-prevention bots and generated video
verification.