README.md

---
license: mit
language:
- en
pretty_name: common-o
dataset_info:
  features:
  - name: image_1
    dtype: image
  - name: image_2
    dtype: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: objects_1
    dtype: string
  - name: objects_2
    dtype: string
  - name: num_objects_image_1
    dtype: int64
  - name: num_objects_image_2
    dtype: int64
  - name: question_template
    dtype: string
  - name: answer_type
    dtype: string
  - name: choices
    dtype: string
  - name: num_choices
    dtype: int64
  - name: num_ground_truth_objects
    dtype: int64
  - name: real_or_synthetic
    dtype: string
  - name: ground_truth_objects
    dtype: string
  splits:
  - name: main
    num_bytes: 5408696753
    num_examples: 10426
  - name: challenge
    num_bytes: 594218345
    num_examples: 12600
  download_size: 1102814055
  dataset_size: 6002915098
configs:
- config_name: default
  data_files:
  - split: main
    path: data/main-*
  - split: challenge
    path: data/challenge-*
---

# Common-O

> measuring multimodal reasoning across scenes


Common-O, inspired by cognitive tests for humans, probes multimodal LLMs' ability to reason across scenes by asking "what’s in common?"

![fair conference content copy.001](https://cdn-uploads.huggingface.co/production/uploads/64c17345e82e55936cf971bc/5av7avUrsBjFuMrWuOiCW.jpeg)

Common-O is comprised of household objects:


![fair conference content copy.003](https://cdn-uploads.huggingface.co/production/uploads/64c17345e82e55936cf971bc/hEvVz2uFR6z-jv1em25eY.jpeg)


We have two subsets: Common-O (3 - 8 objects) and Common-O Complex (8 - 16 objects).

## Multimodal LLMs excel at single image perception, but struggle with multi-scene reasoning


![single_vs_multi_image(1)](https://cdn-uploads.huggingface.co/production/uploads/64c17345e82e55936cf971bc/1cB9iXHrSgyvfXgK6gmGu.png)


## Evaluating a Multimodal LLM on Common-O

```python
import datasets

# get a sample
common_o = datasets.load("facebook/Common-O")["main"]
# common_o_complex = datasets.load("facebook/Common-O")["complex"]
x = common_o[3]

output: str = model(x["image_1"], x["image_2"], x["question"])

check_answer(output, x["answer"])
```

To check the answer, we use an exact match criteria:

```python
import re


def check_answer(
    generation: str,
    ground_truth: List[str]
    ):
    preds = generation.split("\n")[-1]
    preds = re.sub("Answer:", "", preds)
    preds = preds.split(",")
    preds = sorted(preds, key=lambda x: x[0])
    
    ground_truth = sorted(ground_truth)
    return preds == ground_truth
```