Summary of Nl-eye: Abductive Nli For Images, by Mor Ventura et al.
NL-Eye: Abductive NLI for Images
by Mor Ventura, Michael Toker, Nitay Calderon, Zorik Gekhman, Yonatan Bitton, Roi Reichart
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel benchmark, NL-Eye, is introduced to evaluate the visual abductive reasoning skills of Visual Language Models (VLMs). This task requires models to assess the plausibility of hypothesis images based on premise images and explain their decisions. The NL-Eye benchmark consists of 350 carefully curated triplet examples spanning diverse reasoning categories, including physical, functional, logical, emotional, cultural, and social. While humans excel in both plausibility prediction and explanation quality, VLMs struggle significantly, often performing at random baseline levels. This highlights a deficiency in the abductive reasoning capabilities of modern VLMs. The NL-Eye benchmark is an important step towards developing VLMs capable of robust multimodal reasoning for real-world applications, such as accident-prevention bots and generated video verification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new test called NL-Eye helps machines understand how to reason about what might happen if something is done. This is useful for making decisions in the world. The test has 350 examples that are all different, like a floor that’s wet or someone who is happy. Humans do very well on this test, but computers don’t do as well. This shows that computers need to get better at understanding how things work and why they might happen. |
