2024/09/23 by Swathi Shree Narashiman, Narashiman, Swathi Shree, Nitin Chandrachoodan +1 · 4 citations
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Speech Recognition and Synthesis #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2409.15046
openalex publication_date 2024/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.