Data compression language model

In an arXiv research paper titled "Language Modeling Is Compression," researchers detail their discovery that the DeepMind large language model (LLM) called Chinchilla 7.
1. B can perform lossless compression on image patches from the ImageNet image database to 43

In the case of compressing data using language models, the output of the language model provides the probability distribution. There are various statistical compression algorithms such as Huffman coding, arithmetic coding, and asymmetric numeral systems.

This paper describes an original method of doing text-compression, namely by basing the compression algorithm on language models, and using probability esti

Are large language models strong compressors?

Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors.
In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models.

Is there an equivalence between predictive modeling and lossless compression?

Some key points:

There is an equivalence between predictive modeling and lossless compression
based on information theory principles like Shannon's source coding theorem

Maximizing the log-likelihood of a model is equivalent to minimizing the expected code length when using that model for compression via arithmetic coding.

What is a compression perspective on language modeling and large models?

In summary, the paper provides a compression viewpoint on language modeling and large models, demonstrating connections to in-context learning, scaling laws, tokenization, and generation.
Framing prediction as compression encompasses generalization and provides a useful lens.

What is data compression?

Data compression aims to reduce the size of data in a way that maximally preserves the original raw data before compression.
This problem has recently been addressed via neural models , which can achieve more nuanced compression policies than rigidly defined algorithms or en- coding schemes.

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