Summary of Mm-phyrlhf: Reinforcement Learning Framework For Multimodal Physics Question-answering, by Janak Kapuriya et al.
MM-PhyRLHF: Reinforcement Learning Framework for Multimodal Physics Question-Answering
by Janak Kapuriya, Chhavi Kirtani, Apoorv Singh, Jay Saraf, Naman Lal, Jatin Kumar, Adarsh Raj Shivam, Astha Verma, Avinash Anand, Rajiv Ratn Shah
First submitted to arxiv on: 19 Apr 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 Recent advancements in Large Language Models (LLMs) have shown promise in tasks like text summarization and generation, but they often struggle when solving complex physics problems that require arithmetic calculation and conceptual understanding. Moreover, many physics problems involve images containing crucial details necessary to grasp the problem’s context. To address this challenge, we propose an LMM-based chatbot designed to answer multimodal physics Multiple Choice Questions (MCQs). We utilize the MM-PhyQA dataset, comprising Indian high school-level multimodal physics problems, for domain adaptation. To enhance the LMM’s performance, we experiment with two techniques: Reinforcement Learning from Human Feedback (RLHF) and Image Captioning. In image captioning, we provide a detailed explanation of each diagram to minimize hallucinations and image processing errors. The RLHF approach incorporates human feedback into the learning process, improving the model’s problem-solving skills, truthfulness, and reasoning capabilities while minimizing hallucinations. We employ the LLaVA open-source model to answer multimodal physics MCQs and compare its performance with and without using RLHF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a chatbot that can help people understand complex physics problems by answering questions. Physics problems often involve images, which are important for understanding the problem. The researchers used a special kind of artificial intelligence called Large Language Models to create this chatbot. They tested it on a set of physics problems and made some improvements to make it better. The main goal is to help students learn physics more effectively. |
Keywords
» Artificial intelligence » Domain adaptation » Image captioning » Reinforcement learning from human feedback » Rlhf » Summarization
