Summary of Enhancing Llms For Physics Problem-solving Using Reinforcement Learning with Human-ai Feedback, by Avinash Anand et al.
Enhancing LLMs for Physics Problem-Solving using Reinforcement Learning with Human-AI Feedback
by Avinash Anand, Kritarth Prasad, Chhavi Kirtani, Ashwin R Nair, Mohit Gupta, Saloni Garg, Anurag Gautam, Snehal Buldeo, Rajiv Ratn Shah
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The abstract discusses the limitations of Large Language Models (LLMs) in addressing complex physics problems, particularly advanced arithmetic and conceptual understanding. While some research has explored ways to enhance LLMs using prompt engineering and Retrieval Augmentation Generation (RAG), there is still a need to address their limitations in physics reasoning. The paper presents a novel approach called Reinforcement Learning with Human and Artificial Intelligence Feedback (RLHAIF) to improve LLM performance on physics questions. The authors evaluate several reinforcement learning methods, including Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO), and Remax optimization, using the PhyQA dataset. The RLHAIF model is tested on leading LLMs like LLaMA2 and Mistral, achieving superior results, particularly with the MISTRAL-PPO model, demonstrating marked improvements in reasoning and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very smart at understanding text, but they struggle to solve complex physics problems. Some research has tried to help LLMs understand physics better using special techniques, but there is still a lot of room for improvement. This paper presents a new way to improve LLMs’ ability to solve physics problems by training them using human and artificial intelligence feedback. The authors tested this approach on several different models and found that it worked really well, especially with one particular model called MISTRAL-PPO. |
Keywords
» Artificial intelligence » Optimization » Prompt » Rag » Reinforcement learning
