Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models
SKIM framework compresses procedural knowledge in LLMs, maintaining task performance with 30%-60% compression rate.
Key Findings
Methodology
The paper introduces SKIM, an adaptive multi-resolution soft token compression framework for procedural knowledge in LLMs. SKIM creates varying numbers of soft tokens based on skill complexity to enhance inference efficiency while preserving skill usage effectiveness. The method includes three stages: skill reconstruction, procedural warm-up, and skill task alignment to preserve executable dependencies.
Key Results
- SKIM compresses skills to 30%-60% of their original token length while maintaining task performance better than existing methods.
- Experiments on multiple skill datasets show SKIM reduces tokens while maintaining high skill task accuracy.
- Compared to ICAE, SKIM performs better on procedural tasks, avoiding system failures.
Significance
This research offers a novel approach to compress procedural knowledge in LLMs, addressing the inadequacy of existing methods for procedural knowledge compression. By reducing token consumption, SKIM significantly lowers computational and latency costs, especially for frequently updated community skills.
Technical Contribution
SKIM fundamentally differs from existing methods by employing a multi-resolution soft token compression framework, supporting offline self-judgment for optimal resolution selection. It adapts the target model via a LoRA module, ensuring parameter efficiency and supporting multi-skill scenarios.
Novelty
SKIM is the first multi-resolution soft token framework for procedural knowledge compression, retaining procedural dependencies and supporting rapid regeneration and easy transmission compared to methods like ICAE.
Limitations
- SKIM may require higher resolution for extremely complex skills, increasing storage demands.
- Offline selected resolution may not adapt to all inference scenarios.
Future Work
Future work can explore SKIM's performance on larger skill sets and optimize its adaptability across different models and skill complexities.
AI Executive Summary
Large language models face high token consumption and latency costs when dealing with procedural skills, especially those frequently invoked. Existing text compression techniques mainly target factual knowledge in documents and are ineffective for procedural knowledge. To address this, researchers propose the SKIM framework, which uses an adaptive multi-resolution soft token compression method.
SKIM enhances inference efficiency by creating varying numbers of soft tokens based on skill complexity while preserving skill usage effectiveness. Experimental results show SKIM compresses skills to 30%-60% of their original token length while maintaining task performance better than existing methods. This approach not only reduces computational and latency costs but also enables rapid updates and regeneration for large-scale skill sets.
Despite significant progress in skill compression, SKIM may require higher resolution for extremely complex skills, increasing storage demands. Future research can further optimize SKIM's adaptability across different models and skill complexities and explore its performance on larger skill sets.
Deep Analysis
Background
With the widespread application of large language models, effective management and compression of procedural knowledge have become crucial. Traditional text compression methods primarily target factual knowledge in documents, while procedural knowledge presents new challenges due to its complex dependencies and frequent update requirements.
Core Problem
Frequent invocation of procedural skills in large language models leads to high token consumption and latency costs. Existing compression methods fail to effectively preserve procedural dependencies, resulting in execution failures.
Innovation
The SKIM framework addresses procedural knowledge compression through an adaptive multi-resolution soft token method. Its innovations include supporting offline self-judgment for optimal resolution selection and adapting the target model via a LoRA module to ensure parameter efficiency.
Methodology
- �� SKIM framework includes three stages: skill reconstruction, procedural warm-up, and skill task alignment.
- �� Skill reconstruction learns general representations from a large corpus of skills.
- �� Procedural warm-up uses the WikiHow dataset for weakly supervised question answering.
- �� Skill task alignment generates skill-dependent questions using a high-capacity LLM.
Experiments
Experiments were conducted on datasets like BigCodeBench and CHAMP, using Qwen3-8B and Phi-4 as target models. SKIM's compression effects and task performance were validated by comparing it with baseline methods like ICAE and 500xCompressor.
Results
Results show SKIM compresses skills to 30%-60% of their original length while maintaining high task accuracy across multiple datasets. Notably, SKIM outperforms ICAE on procedural tasks.
Applications
SKIM is applicable in scenarios requiring frequent updates and invocation of procedural skills, such as developer tools and automated workflows. Its compression capability significantly reduces computational and latency costs.
Limitations & Outlook
SKIM may require higher resolution for extremely complex skills, increasing storage demands. Additionally, the offline selected resolution may not adapt to all inference scenarios.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking. Every time you cook, you look up a recipe, similar to how a large language model invokes procedural skills. SKIM acts like a smart assistant that compresses recipes into simple steps, allowing you to quickly find key information without flipping through the entire cookbook each time. This saves time and ensures each dish is made perfectly.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game and need to check a guide every time to pass a level. SKIM is like a super guide assistant that compresses the guide into a few key steps, helping you pass levels quickly without flipping through the whole guidebook. Isn't that cool? This way, you can beat the boss faster and become a game master!
Glossary
Large Language Model (LLM)
A large-scale neural network model capable of processing and generating natural language.
Used for adaptive workflows in complex tasks.
Procedural Knowledge
Knowledge about the steps and processes for executing tasks or operations.
Key information to be preserved in skill compression.
Soft Tokens
A compression method that maps text into continuous representations.
Used in SKIM's multi-resolution compression.
LoRA
A low-rank adaptation module for efficient parameter tuning.
Used to adapt the target model during skill task alignment.
WikiHow
A large-scale summarization dataset containing 'how-to' articles from the WikiHow website.
Used for weakly supervised question answering in procedural warm-up.
Open Questions Unanswered questions from this research
- 1 How can SKIM's compression efficiency be further optimized for extremely complex skills?
- 2 How can SKIM's adaptability across different models and skill complexities be enhanced?
Applications
Immediate Applications
Developer Tools
By compressing procedural skills, reduce computational and latency costs in developer tools, enhancing development efficiency.
Long-term Vision
Automated Workflows
Apply SKIM in large-scale automated workflows to significantly reduce resource consumption and achieve more efficient process management.
Abstract
Large language models (LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently, reusable natural language skills have emerged as a popular paradigm to inject procedural knowledge into LLM applications. Since popular skills are often invoked repeatedly, placing their full text in every context significantly increases prefill cost and latency. While text compression techniques have the potential to solve this problem, most existing methods are designed to compress factual knowledge in documents instead of procedural knowledge, making them insufficient for skill compression. In this paper, we argue that an effective skill compression method should: 1) preserve logical dependencies among workflows and tool protocols, 2) enable lightweight, offline compression for frequently updated community skills, and 3) be adaptable to varying complexities across skills. To address this, we present SKIM (SKIll coMpression), an adaptive multi-resolution soft token compression framework for procedural skills. Depending on the complexity of each skill, SKIM creates different numbers of soft tokens that not only improve the efficiency of LLM inference, but also preserve the effectiveness of skill usage. Experiments indicate that SKIM compresses skills to 30 to 60 percent of their original token length while preserving task performance better than existing compression methods.We have released our code at https://github.com/bebr2/SKIM .