Multi-modal Reasoning with LLMs for Visual Semantic Arithmetic
📰 ArXiv cs.AI
Learn to enhance LLMs' visual semantic arithmetic reasoning using multi-modal inputs and reinforcement learning
Action Steps
- Apply reinforcement learning as post-training to LLMs for visual semantic arithmetic tasks
- Use multi-modal inputs combining text and images to enhance reasoning ability
- Configure LLMs to process visual information and infer relationships from images
- Test LLMs on visual semantic arithmetic tasks, such as image analogy problems
- Compare performance of LLMs with and without multi-modal reasoning capabilities
Who Needs to Know This
AI researchers and engineers working on LLMs and multi-modal reasoning can benefit from this knowledge to improve their models' performance
Key Insight
💡 Multi-modal reasoning with LLMs can significantly improve visual semantic arithmetic performance
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🤖 Enhance LLMs' visual semantic arithmetic reasoning with multi-modal inputs & reinforcement learning! 📊
Key Takeaways
Learn to enhance LLMs' visual semantic arithmetic reasoning using multi-modal inputs and reinforcement learning
Full Article
Title: Multi-modal Reasoning with LLMs for Visual Semantic Arithmetic
Abstract:
arXiv:2604.19567v1 Announce Type: new Abstract: Reinforcement learning (RL) as post-training is crucial for enhancing the reasoning ability of large language models (LLMs) in coding and math. However, their capacity for visual semantic arithmetic, inferring relationships from images, remains underexplored. The classic text analogy "king"-"man"+"woman" = "queen" illustrates relational reasoning, yet replacing text with images of "king" and "man" significantly reduces performance because it requir
Abstract:
arXiv:2604.19567v1 Announce Type: new Abstract: Reinforcement learning (RL) as post-training is crucial for enhancing the reasoning ability of large language models (LLMs) in coding and math. However, their capacity for visual semantic arithmetic, inferring relationships from images, remains underexplored. The classic text analogy "king"-"man"+"woman" = "queen" illustrates relational reasoning, yet replacing text with images of "king" and "man" significantly reduces performance because it requir
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