Summary of Aligngpt: Multi-modal Large Language Models with Adaptive Alignment Capability, by Fei Zhao et al.
AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability
by Fei Zhao, Taotian Pang, Chunhui Li, Zhen Wu, Junjie Guo, Shangyu Xing, Xinyu Dai
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed AlignGPT is a novel multimodal large language model designed to address shortcomings in current models’ alignment capabilities. By introducing a two-stage training approach, pre-training and instruction-tuning, AlignGPT learns to recognize varying degrees of alignment between image-text pairs and adaptively adjusts its representation accordingly. This enables the model to excel on 12 benchmarks, demonstrating competitive performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AlignGPT is a new kind of language model that helps computers understand different kinds of information like images and words. Right now, these models are not very good at understanding how well different pieces of information match up. The team behind AlignGPT wanted to change this by creating a model that can learn from examples with different levels of matching. They did this by teaching the model to look at groups of examples based on how well they match and then adjusting its understanding accordingly. This new approach helps the model do better on tasks like recognizing objects in pictures or summarizing texts. |
Keywords
» Artificial intelligence » Alignment » Instruction tuning » Language model » Large language model
