Summary of Robustness-aware Automatic Prompt Optimization, by Zeru Shi et al.
Robustness-aware Automatic Prompt Optimization
by Zeru Shi, Zhenting Wang, Yongye Su, Weidi Luo, Hang Gao, Fan Yang, Ruixiang Tang, Yongfeng Zhang
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 BATprompt method for Large Language Models (LLMs) addresses the limitation of neglecting input perturbations by adversarial training techniques. It leverages LLM’s advanced reasoning capabilities to simulate gradients, guiding prompt generation and optimization on unperturbed inputs. The two-step process demonstrates strong performance on various tasks through iterative optimization. BATprompt outperforms existing methods in robustness and performance under diverse perturbation scenarios. This novel approach shows promise for improving the effectiveness of prompts in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to create prompts that work well even when the input data has mistakes, like typos. The authors wanted to make sure their method works on any kind of input, not just perfect ones. They came up with an idea called BATprompt, which uses special training techniques to generate good prompts. This method doesn’t need to know how the model’s inside works, unlike other methods that try to fool the model by making mistakes. Instead, it relies on the model’s ability to understand language and reflect on its own performance. The authors tested their method on several datasets and found that it does a better job than other methods in handling different kinds of input errors. |
Keywords
» Artificial intelligence » Optimization » Prompt
