Summary of Markov Chain Of Thought For Efficient Mathematical Reasoning, by Wen Yang et al.
Markov Chain of Thought for Efficient Mathematical Reasoning
by Wen Yang, Minpeng Liao, Kai Fan
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Markov Chain of Thought (MCoT) is proposed to enhance the mathematical reasoning capabilities of large language models. By conceptualizing the standard multi-step CoT as a Markov chain, MCoT enables efficient next-step inference without relying on lengthy knowledge caches. The approach involves compressing previous reasoning steps into simplified questions and interacting with code interpreters for self-correction. Empirical results demonstrate that MCoT significantly enhances efficiency while maintaining comparable accuracy. This work paves the way for exploring long CoT reasoning abilities of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting better at math! Researchers have created a new way to help them reason and solve problems more efficiently. They call it Markov Chain of Thought (MCoT). MCoT is like a special kind of memory that helps the model learn from its mistakes and make fewer errors. It’s like when you’re doing a puzzle and you look back at your previous steps to figure out where you went wrong. This new way of thinking makes LLMs faster and better at solving math problems. |
Keywords
» Artificial intelligence » Inference
