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LLMs achieve 82% better source code compression than general tools

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for lossless source code compression using Large Language Models (LLMs) that improves upon existing techniques. This approach, called Thresholded Symbol Ranking, bounds LLM predictions to the top-ranked symbols, storing exceptions separately. Evaluations across 30 LLMs show this method achieves up to 37% better compression ratios and is 40% faster than previous LLM-based compressors. While still slower than general-purpose compressors like zstd, it offers a significantly improved compression gain of up to 82% relative to them, demonstrating LLMs' effectiveness in capturing source code's unique regularities. AI

IMPACT Offers a new trade-off point between compression ratio and speed for large-scale code archives, potentially impacting storage costs and data transfer efficiency.

RANK_REASON Academic paper detailing a novel method for source code compression using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs achieve 82% better source code compression than general tools

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Academic paper detailing a novel method for source code compression using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. arXiv cs.CL TIER_1 English(EN) · Angelo Nardone, Paolo Ferragina ·

    LLM-based Source Code Compression via Thresholded Symbol Ranking

    arXiv:2607.24192v1 Announce Type: cross Abstract: We study the problem of lossless compression of source code, motivated by the storage demands of large-scale software archives, such as Software Heritage (https://www.softwareheritage.org/). General-purpose compressors (e.g., zstd…

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