Papers with Qwen2.5-14B-Instruct
TokenSkip: Controllable Chain-of-Thought Compression in LLMs (2025.emnlp-main)
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| Challenge: | Chain-of-Thought (CoT) has been proven effective in enhancing the reasoning capabilities of large language models (LLMs). |
| Approach: | They propose a chain-of-thought (CoT) prompting approach that enables LLMs to selectively skip less important tokens, allowing for controllable CoT compression. |
| Outcome: | Experiments show that TokenSkip reduces CoT token usage while preserving strong reasoning performance. |