Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability
This addresses inference cost and interpretability issues for users of LLMs in code and logic tasks, offering a novel approach with strong specific gains.
The paper tackles token efficiency bottlenecks in large language models for code generation and logical reasoning by proposing a symbolic compression framework, achieving a 78.3% token compression rate and improving logical traceability by 62%.
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.