Achieving More With Less Using Token Compression - Edgee Blog

Token compression is an emerging field focused on reducing the length of prompts for large language models (LLMs) while maintaining their intended functionality and task performance. This involves techniques like token-level compression, which shortens input sequences by rewriting or summarizing text, and embedding-level compression, which operates in continuous space to blend token representations into fewer dense vectors. The goal of these methods is to minimize costs and resource usage without compromising the quality of the output. The document outlines various techniques for achieving token compression, including filtering, dedupe clustering, paraphrasing, selective retrieval, and distillation. Each technique has its trade-offs between precision and efficiency, and the choice of method depends on the specific use case and available resources. The challenge lies in balancing the reduction of tokens with the preservation of the prompt's behavior, alignment, constraints, and accuracy, making token compression an essential consideration in modern AI system design.