Summary of Grl-prompt: Towards Knowledge Graph Based Prompt Optimization Via Reinforcement Learning, by Yuze Liu et al.
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning
by Yuze Liu, Tingjie Liu, Tiehua Zhang, Youhua Xia, Jinze Wang, Zhishu Shen, Jiong Jin, Fei Richard Yu
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large language models (LLMs) have revolutionized natural language processing (NLP), leveraging their vast general knowledge to excel in various tasks. However, recent studies reveal that LLMs’ performance heavily relies on the input prompt, often requiring manual and labor-intensive prompt engineering efforts. To address these limitations and unlock the full potential of LLMs, we introduce GRL-Prompt, a novel framework for prompt optimization via reinforcement learning (RL) in an end-to-end manner. Our approach constructs a knowledge graph (KG) to better encode the correlation between user queries and candidate in-context examples, allowing for structured action/state representation during prompt optimization. A policy network generates optimal actions by selecting in-context examples in a rewardable order to construct prompts. Additionally, embedding-based reward shaping stabilizes the RL training process. Experimental results demonstrate GRL-Prompt’s superiority over recent state-of-the-art methods, achieving significant increases in ROUGE-1 (0.10), ROUGE-2 (0.07), ROUGE-L (0.07), and BLEU (0.05) metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super smart computer that can understand and respond to language like humans do. However, these computers need help getting the right answers from us, which is called “prompt engineering.” A new approach called GRL-Prompt helps make this process easier by using a type of learning called reinforcement learning. This method allows the computer to figure out the best way to ask questions and get good responses on its own. The researchers who developed GRL-Prompt tested it and found that it worked better than other methods, getting 0.10 points higher in one measure and 0.05 points higher in another. |
Keywords
» Artificial intelligence » Bleu » Embedding » Knowledge graph » Natural language processing » Nlp » Optimization » Prompt » Reinforcement learning » Rouge
