Summary of Llms Can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-thought, By Zhuoxuan Jiang and Haoyuan Peng and Shanshan Feng and Fan Li and Dongsheng Li
LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought
by Zhuoxuan Jiang, Haoyuan Peng, Shanshan Feng, Fan Li, Dongsheng Li
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach to mitigate hallucination in Large Language Models (LLMs) is proposed, focusing on mistake detection as the initial step. However, existing research highlights that simplistic prompting strategies often struggle to reliably identify reasoning mistakes. To address this challenge, a unique Pedagogical Chain-of-Thought (PedCoT) prompting strategy is introduced, inspired by educational theory. PedCoT consists of pedagogical principles for prompts (PPP), two-stage interaction process (TIP), and grounded PedCoT prompts, all rooted in the Bloom Cognitive Model (BCM). The approach is evaluated on two public datasets featuring math problems of varying difficulty levels. Experimental results demonstrate that the zero-shot prompting strategy outperforms strong baselines, achieving reliable mathematical mistake identification and providing a foundation for automatic math answer grading. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help Large Language Models (LLMs) get better at math is presented. The idea is to teach LLMs to identify mistakes in their thinking. Right now, these models struggle to do this with simple instructions. To fix this, a special set of questions and interactions is designed to guide the LLM’s mistake detection. This approach uses educational theories to help the LLM understand what it’s doing wrong and how to correct its mistakes. The results show that this method works well on math problems of different levels of difficulty. |
Keywords
» Artificial intelligence » Hallucination » Prompting » Zero shot
