Lossless Prompt Compression via Dictionary-Encoding and In-Context Learning: Enabling Cost-Effective LLM Analysis of Repetitive Data

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, extended

Summary

A new training-free approach enables lossless prompt compression for Large Language Models (LLMs) by leveraging in-context learning. The method uses dictionary encoding, replacing frequently occurring subsequences with compact meta-tokens and providing the compression dictionary in the system prompt. This allows LLMs to interpret meta-tokens and perform analysis on encoded representations, yielding outputs equivalent to uncompressed inputs. The proposed hierarchical compression algorithm identifies repetitive patterns at multiple length scales and incorporates a token-savings optimization criterion to ensure cost reduction. It achieves compression ratios up to 80% on the LogHub 2.0 benchmark. Evaluation using Claude 3.7 Sonnet shows exact match rates exceeding 0.99 for template-based compression and average Levenshtein similarity scores above 0.91 for algorithmic compression, even at 60%–80% compression ratios. Compression ratio explains less than 2% of variance in similarity metrics, indicating that decompression quality depends on dataset characteristics rather than compression intensity.

Key takeaway

For Machine Learning Engineers processing large volumes of repetitive data with API-based LLMs, this method offers a direct path to significant cost savings and improved context utilization. You can achieve 60%–80% token reduction without fine-tuning models, simply by providing a compression dictionary in the system prompt. This enables cost-effective analysis of datasets that were previously too expensive or large, allowing for more comprehensive data processing within existing LLM integrations.

Key insights

LLMs can learn dictionary encodings in-context, enabling lossless prompt compression without fine-tuning and preserving analytical accuracy.

Principles

• Dictionary encoding preserves semantic completeness.
• Compression effectiveness correlates with log structure regularity.
• Decompression quality depends on dataset properties, not compression intensity.

Method

A hierarchical dictionary-encoding algorithm identifies repetitive subsequences at multiple length scales, applies replacements in descending order of token savings, and uses a token-savings optimization criterion to prevent dictionary overhead from exceeding savings.

In practice

• Achieve 60%–80% LLM API cost reductions.
• Fit more data into fixed context windows.
• Apply to any domain with repetitive textual patterns.

Topics

• Lossless Prompt Compression
• In-Context Learning
• Dictionary Encoding
• Large Language Models
• Repetitive Data Analysis

Best for: Machine Learning Engineer, NLP Engineer, CTO, AI Scientist, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.