Summary of Boosting Private Domain Understanding Of Efficient Mllms: a Tuning-free, Adaptive, Universal Prompt Optimization Framework, by Jiang Liu et al.
Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework
by Jiang Liu, Bolin Li, Haoyuan Li, Tianwei Lin, Wenqiao Zhang, Tao Zhong, Zhelun Yu, Jinghao Wei, Hao Cheng, Wanggui He, Fangxun Shu, Hao Jiang, Zheqi Lv, Juncheng Li, Siliang Tang, Yueting Zhuang
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper proposes an Efficient Multimodal Large Language Model (EMLLM) adaptation framework for private domains, focusing on reducing data requirements and avoiding parameter fine-tuning. The framework, called Adaptive Prompt Optimization Framework (APOF), consists of two stages: Predefined Prompt, which generates a prompt optimization strategy tree using reinforcement searching, and Prompt Reflection, which initializes the prompt based on optimization priors and refines it through self-reflection. APOF elegantly generates “ideal prompts” for processing private domain-specific data without requiring parameter fine-tuning or excessive data. Extensive experiments demonstrate that APOF improves efficiency and performance compared to baselines across multiple tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with big language models that can’t be used in some situations because they need special information to work well. The solution is called Adaptive Prompt Optimization Framework (APOF). It makes sure the model gets the right prompts, or instructions, without needing too much data or changing its own settings. This means APOF can help big language models work better and faster in places where they wouldn’t normally be able to. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Optimization » Prompt
