GPT models trained on a corpus are shown to approximate a tree of empirical next-token probabilities derived from the same corpus, with alignment increasing with model size.
AlphaZip: Neural Network-Enhanced Lossless Text Compression
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Data compression continues to evolve, with traditional information theory methods being widely used for compressing text, images, and videos. Recently, there has been growing interest in leveraging Generative AI for predictive compression techniques. This paper introduces a lossless text compression approach using a Large Language Model (LLM). The method involves two key steps: first, prediction using a dense neural network architecture, such as a transformer block; second, compressing the predicted ranks with standard compression algorithms like Adaptive Huffman, LZ77, or Gzip. Extensive analysis and benchmarking against conventional information-theoretic baselines demonstrate that neural compression offers improved performance.
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GPT as a Monte Carlo Language Tree: A Probabilistic Perspective
GPT models trained on a corpus are shown to approximate a tree of empirical next-token probabilities derived from the same corpus, with alignment increasing with model size.