Summary of Augmenting Multimodal Llms with Self-reflective Tokens For Knowledge-based Visual Question Answering, by Federico Cocchi et al.
Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering
by Federico Cocchi, Nicholas Moratelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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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 This paper introduces a novel method to enhance the adaptability of Multimodal Large Language Models (MLLMs) by integrating external knowledge sources. The proposed model, Reflective LLaVA (ReflectiVA), utilizes reflective tokens to dynamically determine the need for external knowledge and predict the relevance of information retrieved from an external database. This enables the MLLM to manage external knowledge while preserving fluency and performance on tasks where external knowledge is not needed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make Multimodal Large Language Models better at using information from the internet. It creates a new model called Reflective LLaVA that can decide when it needs help from outside sources and what’s important. This helps the model work well on tasks where it doesn’t need extra information, too. |
