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---
license: apache-2.0
language:
- en
tags:
- vlm
- reasoning
- multimodal
- nli
size_categories:
- n<1K
task_categories:
- visual-question-answering
---
# **NL-Eye Benchmark**
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. The dataset contains **350 carefully curated triplet examples** (1,050 images) spanning diverse reasoning categories, temporal categories and domains.
NL-Eye represents a crucial step toward developing **VLMs capable of robust multimodal reasoning** for real-world applications, such as accident-prevention bots and generated video verification.
project page: [NL-Eye project page](https://venturamor.github.io/NLEye/)
preprint: [NL-Eye arxiv](https://arxiv.org/abs/2410.02613)
---
## **Dataset Structure**
The dataset contains:
- A **CSV file** with annotations (`test_set.csv`).
- An **images directory** with subdirectories for each sample (`images/`).
### **CSV Fields:**
| Field | Type | Description |
|--------------------------------|----------|----------------------------------------------------------------|
| `sample_id` | `int` | Unique identifier for each sample. |
| `reasoning_category` | `string` | One of the six reasoning categories (physical, functional, logical, emotional, cultural, or social). |
| `domain` | `string` | One of the ten domain categories (e.g., education, technology). |
| `time_direction` | `string` | One of three directions (e.g., forward, backward, parallel). |
| `time_duration` | `string` | One of three durations (e.g., short, long, parallel). |
| `premise_description` | `string` | Description of the premise. |
| `plausible_hypothesis_description` | `string` | Description of the plausible hypothesis. |
| `implausible_hypothesis_description` | `string` | Description of the implausible hypothesis. |
| `gold_explanation` | `string` | The gold explanation for the sample's plausibility. |
| `additional_valid_human_explanations` | `string` (optional) | Extra human-generated (crowd-workers) explanations for explanation diversity. |
> **Note**: Not all samples contain `additional_valid_human_explanations`.
---
### **Images Directory Structure:**
The `images/` directory contains **subdirectories named after each `sample_id`**. Each subdirectory includes:
- **`premise.png`**: Image showing the premise.
- **`hypothesis1.png`**: Plausible hypothesis.
- **`hypothesis2.png`**: Implausible hypothesis.
## **Usage**
This dataset is **only for test purposes**.
### Citation
```bibtex
@misc{ventura2024nleye,
title={NL-Eye: Abductive NLI for Images},
author={Mor Ventura and Michael Toker and Nitay Calderon and Zorik Gekhman and Yonatan Bitton and Roi Reichart},
year={2024},
eprint={2410.02613},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
|