ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering
📰 ArXiv cs.AI
ReAG is a reasoning-augmented generation model for knowledge-based visual question answering
Action Steps
- Retrieve external documents relevant to the query
- Condition the answer generation process using the retrieved documents
- Use reasoning-augmented generation to produce accurate answers
- Fine-tune the model on knowledge-based VQA tasks to improve performance
Who Needs to Know This
AI researchers and engineers working on multimodal large language models can benefit from ReAG, as it enhances the model's ability to answer domain-specific and knowledge-intensive queries
Key Insight
💡 ReAG enhances the ability of multimodal large language models to answer domain-specific and knowledge-intensive queries by leveraging external knowledge
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🤖 ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering
Key Takeaways
ReAG is a reasoning-augmented generation model for knowledge-based visual question answering
Full Article
Title: ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering
Abstract:
arXiv:2511.22715v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have shown impressive capabilities in jointly understanding text, images, and videos, often evaluated via Visual Question Answering (VQA). However, even state-of-the-art MLLMs struggle with domain-specific or knowledge-intensive queries, where relevant information is underrepresented in pre-training data. Knowledge-based VQA (KB-VQA) addresses this by retrieving external documents to condition answ
Abstract:
arXiv:2511.22715v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have shown impressive capabilities in jointly understanding text, images, and videos, often evaluated via Visual Question Answering (VQA). However, even state-of-the-art MLLMs struggle with domain-specific or knowledge-intensive queries, where relevant information is underrepresented in pre-training data. Knowledge-based VQA (KB-VQA) addresses this by retrieving external documents to condition answ
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