Summary of Teaching-inspired Integrated Prompting Framework: a Novel Approach For Enhancing Reasoning in Large Language Models, by Wenting Tan et al.
Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models
by Wenting Tan, Dongxiao Chen, Jieting Xue, Zihao Wang, Taijie Chen
First submitted to arxiv on: 10 Oct 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 recent study highlights the limitations of Large Language Models (LLMs) in arithmetic reasoning tasks, despite their impressive performance across various domains. To address this issue, researchers propose a novel Teaching-Inspired Integrated Framework that emulates the instructional process of a teacher guiding students. This framework equips LLMs with essential concepts, relevant theorems, and similar problems with analogous solution approaches, enhancing their reasoning abilities. The study introduces two new Chinese datasets, MathMC and MathToF, along with detailed explanations and answers. Experiments on nine benchmarks demonstrate that this approach improves the reasoning accuracy of LLMs, achieving state-of-the-art performance on four math benchmarks using GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super-smart AI system that can solve math problems like a human teacher would guide you through them. This new research aims to make Large Language Models better at solving arithmetic reasoning tasks. To do this, the researchers created a special framework that helps the AI learn by showing it how to break down math problems into smaller steps and apply similar solution approaches to similar problems. The study also introduces two new datasets of Chinese math problems, along with answers, to help test the AI’s skills. The results show that this approach makes the AI better at solving math problems, reaching a new level of accuracy on four specific math benchmarks. |
Keywords
» Artificial intelligence » Gpt
