How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach
The paper studies reasoning length and model performance tradeoff. It explores compression strategies for large language models (LLMs). Token complexity measures minimal tokens for successful problem-solving. LLMs adapt response length based on problem difficulty. Compression improvements require matching token-length to token complexity. Shorter prompts can maintain accuracy with reduced response length.