Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability

• URL: http://arxiv.org/abs/2501.18657v1
• Date: Thu, 30 Jan 2025 06:40:52 GMT
• Title: Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability
• Authors: Lumen AI, Tengzhou No. 1 Middle School, Shihao Ji, Zihui Song, Fucheng Zhong, Jisen Jia, Zhaobo Wu, Zheyi Cao, Tianhao Xu,
• Abstract summary: Large language models (LLMs) face significant token efficiency bottlenecks in code generation and logical reasoning tasks. This paper proposes a formal framework based on symbolic compression,integrating logic, information-theoretic optimal encoding, and context-aware inference techniques.
• Score: 3.9122242678047456
• License:
• Abstract: Large language models (LLMs) face significant token efficiency bottlenecks in code generation and logical reasoning tasks, a challenge that directly impacts inference cost and model interpretability. This paper proposes a formal framework based on symbolic compression,integrating combinatory logic, information-theoretic optimal encoding, and context-aware inference techniques to achieve a step-change improvement in token efficiency while preserving semantic integrity. We establish a mathematical framework within a functional programming paradigm, derive the quantitative relationship between symbolic density and model interpretability, and propose a differentiable compression factor metric to evaluate encoding efficiency. Furthermore, we leverage parameter-efficient fine-tuning (PEFT) techniques to achieve a low-cost application of the GAEL language. Experimental results show that this method achieves a 78.3% token compression rate in code generation tasks while improving logical traceability by 62% through structural explicitness. This research provides new theoretical tools for efficient inference in LLMs and opens a symbolic path for modelinterpretability research.

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