Summary of Commit: Coordinated Instruction Tuning For Multimodal Large Language Models, by Junda Wu et al.
CoMMIT: Coordinated Instruction Tuning for Multimodal Large Language Models
by Junda Wu, Xintong Li, Tong Yu, Yu Wang, Xiang Chen, Jiuxiang Gu, Lina Yao, Jingbo Shang, Julian McAuley
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 A multimodal large language model (MLLM) is a type of artificial intelligence that combines natural language processing with computer vision or audio processing to perform various tasks. The goal of instruction tuning in MLLMs is to harmonize the learning process between the backbone LLM and a pre-trained feature encoder for downstream applications. This paper investigates the challenges and opportunities in this area, finding that an imbalance in learning between the two components can lead to sub-optimal results due to diminished learning gradients. To address this issue, the authors propose a measurement to evaluate the learning balance and design a dynamic learning scheduler that coordinates the learning process. Additionally, they introduce an auxiliary loss regularization method to promote updating of the generation distribution considering the learning state of each component. The proposed techniques are model-agnostic and can be integrated with various MLLM backbones. Experimental results demonstrate the effectiveness and efficiency of the approach on multiple downstream tasks and modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are a type of artificial intelligence that can understand and generate human-like text, as well as perform other tasks. The goal of instruction tuning is to teach these models new skills by combining their abilities with those of other AI systems. This paper looks at the challenges of teaching large language models new things, finding that it’s easy to get stuck or not learn effectively if the learning process isn’t balanced between different parts of the model. To solve this problem, the authors propose a way to measure and adjust the learning process so that all parts of the model are working together smoothly. They also suggest ways to help the model learn more accurately and efficiently. The results show that their approach works well on various tasks and can be used with different types of AI models. |
Keywords
* Artificial intelligence * Encoder * Instruction tuning * Large language model * Natural language processing * Regularization
