Summary of Beyond Anti-forgetting: Multimodal Continual Instruction Tuning with Positive Forward Transfer, by Junhao Zheng et al.
Beyond Anti-Forgetting: Multimodal Continual Instruction Tuning with Positive Forward Transfer
by Junhao Zheng, Qianli Ma, Zhen Liu, Binquan Wu, Huawen Feng
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes a method called Prompt Tuning with Positive Forward Transfer (Fwd-Prompt) to enable Multimodal Large Language Models (MLLMs) to adapt to continuously emerging requirements without expensive retraining. The approach addresses the issues of catastrophic forgetting and negative forward transfer, which occur when MLLMs forget old knowledge or perform poorly on new tasks. Fwd-Prompt uses prompt-based learning to minimize interference between tasks and reuse pre-trained knowledge, achieving state-of-the-art performance while updating fewer parameters and requiring no old samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a method called Prompt Tuning with Positive Forward Transfer (Fwd-Prompt) that helps large language models learn new things without forgetting what they already know. This is important because it makes these models more useful for real-world applications where tasks are constantly changing. The researchers discovered a problem in the way these models process information, which caused them to forget old knowledge and perform poorly on new tasks. To solve this problem, they created Fwd-Prompt, a method that uses prompts (short phrases) to help the model learn what’s important for each task without interfering with its understanding of previous tasks. |
Keywords
* Artificial intelligence * Prompt
