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---
license: cc-by-nc-sa-4.0
language:
- en
annotations_creators:
- no-annotation
task_categories:
- text-generation
task_ids:
- language-modeling
size_categories:
- 10K<n<100K
configs:
- config_name: python
data_files:
- split: test
path:
- data/python.jsonl
- config_name: cc
data_files:
- split: test
path:
- data/cc.jsonl
- config_name: arxiv_math
data_files:
- split: test
path:
- data/arxiv_math.jsonl
---
This is the compression corpora dataset used in the paper "Compression Represents Intelligence Linearly".
We find that LLMs’ intelligence – reflected by benchmark scores – almost **linearly** correlates with their ability to compress external text corpora. We measure intelligence along three key abilities: knowledge and commonsense, coding, and mathematical reasoning, and provide the corresponding compression corpora here respectively named cc, python, and arxiv_math.
### Load the data
```python
from datasets import load_dataset
dataset=load_dataset(r"hkust-nlp/llm-compression",name="python")
print(dataset['test'][0])
```
More details on compression evaluation are at our [github page](https://github.com/hkust-nlp/llm-compression-intelligence).
### Citation
```
@misc{huang2024compression,
title={Compression Represents Intelligence Linearly},
author={Yuzhen Huang and Jinghan Zhang and Zifei Shan and Junxian He},
year={2024},
eprint={2404.09937},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```