Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Sub-tasks:
language-modeling
Languages:
English
Size:
10K - 100K
ArXiv:
License:
Update README.md
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README.md
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---
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license: cc-by-nc-sa-4.0
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---
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---
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license: cc-by-nc-sa-4.0
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language:
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- en
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annotations_creators:
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- no-annotation
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task_categories:
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- text-generation
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task_ids:
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- language-modeling
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size_categories:
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- 10K<n<100K
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---
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This is the dataset for the paper Compression Represents Intelligence Linearly.
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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 corresponding datasets here respectively named cc, python, and arxiv_math.
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### Load the data
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```python
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from datasets import load_dataset
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dataset=load_dataset(r"hkust-nlp/cpt",name="python")
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print(dataset['test'][0])
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```
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More details on compression evaluation are at our [github page](https://github.com/hkust-nlp/cpt).
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### Citation
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```
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@xxxx
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```
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