Summary of Reprompt: Planning by Automatic Prompt Engineering For Large Language Models Agents, By Weizhe Chen et al.
RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
by Weizhe Chen, Sven Koenig, Bistra Dilkina
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed paper introduces a novel method called RePrompt, which optimizes step-by-step instructions in prompts given to Large Language Model (LLM) agents. The approach leverages intermediate feedback from interactions with LLM agents and eliminates the need for a final solution checker. This addresses the challenge of evaluating prompt performance in domains where traditional evaluation methods are not feasible. By applying RePrompt, the authors demonstrate improved performance on various reasoning tasks, including PDDL generation, TravelPlanner, and Meeting Planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have been successful outside their traditional natural language processing domain. The prompts given to LLMs greatly affect what they generate, making automatic prompt engineering (APE) crucial for many researchers and users. However, previous APE methods rely on a final checker, which is difficult to implement with LLM agents. This paper proposes RePrompt, an innovative approach that optimizes prompts step-by-step using intermediate feedback from interactions with LLM agents. By doing so, it eliminates the need for a final solution checker. The authors show that RePrompt can generally improve performance on various reasoning tasks. |
Keywords
» Artificial intelligence » Large language model » Natural language processing » Prompt
