Summary of Llm As a Complementary Optimizer to Gradient Descent: a Case Study in Prompt Tuning, by Zixian Guo et al.
LLM as a Complementary Optimizer to Gradient Descent: A Case Study in Prompt Tuning
by Zixian Guo, Ming Liu, Zhilong Ji, Jinfeng Bai, Yiwen Guo, Wangmeng Zuo
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 novel optimization framework is proposed, combining the strengths of both gradient-based optimizers and Large Language Models (LLMs). The former serves as a disciplined doer, making locally optimal updates, while LLMs act as high-level mentors, inferring better solutions from natural language instructions. The collaborative optimization process alternates between these two approaches, leveraging parameter trajectories recorded during previous stages to restart the gradient-based optimizer. This framework is evaluated on prompt tuning and consistently outperforms competitive baselines across various tasks, demonstrating the synergistic effect of conventional gradient-based optimization and LLMs’ inference ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to solve complex problems is discovered by combining two types of optimizers: one that makes small steps towards a solution and another that uses language to find better solutions. The first optimizer makes local changes, while the second looks at natural language instructions to improve the search. By alternating between these two approaches, the framework finds better solutions than previous methods on different tasks. |
Keywords
» Artificial intelligence » Inference » Optimization » Prompt
