Boosting LLM Efficiency Through Symbolic Compression
A formal approach to enhance token efficiency while maintaining interpretability
This research presents a formal framework for improving the token efficiency of large language models in code generation and logical reasoning tasks.
- Integrates combinatory logic and information-theoretic encoding to optimize token usage
- Addresses critical bottlenecks affecting inference costs and model interpretability
- Preserves semantic integrity while achieving significant efficiency improvements
- Provides a mathematical foundation for more efficient LLM operations
For engineering teams, this approach offers a pathway to reduce computational resources required for LLM deployment while maintaining or enhancing performance on complex reasoning tasks.