task_categories:text-classification size_categories:n<1K language:en license:mit creative problem solving puzzles fixation effect large language models only connect quiz show connecting walls arxiv:2306.11167 region:us
The Only Connect Wall (OCW) dataset contains 618 "Connecting Walls" from the Round 3: Connecting Wall segment of the Only Connect quiz show, collected from 15 seasons' worth of episodes. Each wall contains the ground-truth groups and connections as well as recorded human performance.
@article{Naeini2023LargeLM,
    title        = {Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset},
    author       = {Saeid Alavi Naeini and Raeid Saqur and Mozhgan Saeidi and John Giorgi and Babak Taati},
    year         = 2023,
    journal      = {ArXiv},
    volume       = {abs/2306.11167},
    url          = {https://api.semanticscholar.org/CorpusID:259203717}
}
---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- creative problem solving
- puzzles
- fixation effect
- large language models
- only connect
- quiz show
- connecting walls
pretty_name: Only Connect Wall Dataset
size_categories:
- n<1K
---
# Only Connect Wall (OCW) Dataset

The Only Connect Wall (OCW) dataset contains 618 _"Connecting Walls"_ from the [Round 3: Connecting Wall](https://en.wikipedia.org/wiki/Only_Connect#Round_3:_Connecting_Wall) segment of the [Only Connect quiz show](https://en.wikipedia.org/wiki/Only_Connect), collected from 15 seasons' worth of episodes. Each wall contains the ground-truth __groups__ and __connections__ as well as recorded human performance. Please see [our paper](https://arxiv.org/abs/2306.11167) and [GitHub repo](https://github.com/TaatiTeam/OCW) for more details about the dataset and its motivations.

## Usage

```python
# pip install datasets
from datasets import load_dataset

dataset = load_dataset("TaatiTeam/OCW")

# The dataset can be used like any other HuggingFace dataset
# E.g. get the wall_id of the first example in the train set
dataset["train"]["wall_id"][0]
# or get the words of the first 10 examples in the test set
dataset["test"]["words"][0:10]
```

We also provide two different versions of the dataset where the red herrings in each wall have been significantly reduced (`ocw_randomized`) or removed altogether (`ocw_wordnet`) which can be loaded like:

```python
# pip install datasets
from datasets import load_dataset

ocw_randomized = load_dataset("TaatiTeam/OCW", "ocw_randomized")
ocw_wordnet = load_dataset("TaatiTeam/OCW", "ocw_wordnet")
```

See [our paper](https://arxiv.org/abs/2306.11167) for more details.

## 📝 Citing

If you use the Only Connect dataset in your work, please consider citing our paper:

```
@article{Naeini2023LargeLM,
    title        = {Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset},
    author       = {Saeid Alavi Naeini and Raeid Saqur and Mozhgan Saeidi and John Giorgi and Babak Taati},
    year         = 2023,
    journal      = {ArXiv},
    volume       = {abs/2306.11167},
    url          = {https://api.semanticscholar.org/CorpusID:259203717}
}
```

## 🙏 Acknowledgements

We would like the thank the maintainers and contributors of the fan-made and run website [https://ocdb.cc/](https://ocdb.cc/) for providing the data for this dataset. We would also like to thank the creators of the Only Connect quiz show for producing such an entertaining and thought-provoking show.