Summary of Improving Physics Reasoning in Large Language Models Using Mixture Of Refinement Agents, by Raj Jaiswal et al.
Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents
by Raj Jaiswal, Dhruv Jain, Harsh Parimal Popat, Avinash Anand, Abhishek Dharmadhikari, Atharva Marathe, Rajiv Ratn Shah
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 In this paper, researchers tackle the challenges faced by Large Language Models (LLMs) when applying scientific reasoning, particularly in physics. The models struggle with problem miscomprehension, incorrect concept application, and computational errors. To address these issues simultaneously, they introduce Mixture of Refinement Agents (MoRA), a framework that iteratively refines LLM-generated solutions using refinement agents guided by GPT-4o error identification. MoRA improves the performance of open-source LLMs like Llama-3-70B and Gemma-2-27B on physics datasets, achieving up to 16% increased accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Language Models better understand complex physics problems. Right now, these models struggle when trying to solve physics questions because they don’t always get the problem right, use the wrong concepts, or make math mistakes. To fix this, scientists created a new way called Mixture of Refinement Agents (MoRA). MoRA helps LLMs by correcting their mistakes and giving them better answers. This is important because it makes LLMs more accurate when answering physics questions. |
Keywords
» Artificial intelligence » Gpt » Llama
