Datasets:
File size: 2,605 Bytes
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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?"
Common-O is comprised of household objects:
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
## 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
```
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