Summary of Qpo: Query-dependent Prompt Optimization Via Multi-loop Offline Reinforcement Learning, by Yilun Kong et al.
QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning
by Yilun Kong, Hangyu Mao, Qi Zhao, Bin Zhang, Jingqing Ruan, Li Shen, Yongzhe Chang, Xueqian Wang, Rui Zhao, Dacheng Tao
First submitted to arxiv on: 20 Aug 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 introduces Query-dependent Prompt Optimization (QPO), a novel approach to optimizing large language model (LLM) prompts for improved performance. The authors highlight that current prompt optimization methods focus solely on task-level performance, neglecting the importance of query-preferred prompts, which can lead to suboptimal results. To address this limitation, QPO leverages offline reinforcement learning to iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to input queries. This approach circumvents the need for frequent interactions with LLMs and reduces redundant interaction costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have shown remarkable success in various tasks, but prompt engineering has been overlooked as an important aspect of their performance. The authors introduce Query-dependent Prompt Optimization (QPO), a method that uses offline reinforcement learning to generate optimal prompts for LLMs. This approach improves the prompting effect and reduces redundant interaction costs. |
Keywords
» Artificial intelligence » Language model » Large language model » Optimization » Prompt » Prompting » Reinforcement learning
