TokenSkip: Optimizing Chain-of-Thought Reasoning in LLMs Through Controllable Token Compression

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Large Language Models (LLMs) struggle with computational overhead in complex reasoning tasks due to lengthy Chain-of-Thought (CoT) sequences. TokenSkip, developed by researchers from The Hong Kong Polytechnic University and the University of Science and Technology of China, aims to optimize CoT processing by skipping less important tokens while preserving critical reasoning tokens. This method reduces computational costs significantly with minimal performance degradation, showing promising results in early testing compared to other approaches like prompt-based reduction and truncation.