README.md

---
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}
}
```